Generative AI for Autonomous Driving: A Review

Abhishek Vivekanandan; Adam Molin; Annajoyce Mariani; Bojan Derajic; Christian Schlauch; Christian Wirth; Dennis Rochau; Fabian B. Flohr; Firas Mualla; Giovanni Lucente

REVIEW 3 major objections 4 minor 2 cited by

Generative AI for Autonomous Driving: A Review

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A structured survey argues that generative models now span map creation, scenario generation, trajectory prediction, and motion planning, with safety, interpretability, and real-time limits as the open barriers to deployment.

desk verdict A broad, readable survey that is worth reading as an orientation, but its recommendations section carries an unsourced leaderboard claim that strains against the paper's own closed-loop caveats. read the letter →

arxiv 2505.15863 v1 pith:JXJYNMSM submitted 2025-05-21 cs.CV cs.AIcs.RO

Katharina Winter , Abhishek Vivekanandan , Rupert Polley , Yinzhe Shen , Christian Schlauch , Mohamed-Khalil Bouzidi , Bojan Derajic , Natalie Grabowsky

show 12 more authors

Annajoyce Mariani Dennis Rochau Giovanni Lucente Harsh Yadav Firas Mualla Adam Molin Sebastian Bernhard Christian Wirth Ömer Şahin Taş Nadja Klein Fabian B. Flohr Hanno Gottschalk

This is my paper

classification cs.CVcs.AIcs.RO

keywords generativeAIautonomousdrivingmotionplanningtrajectorypredictionscenariogenerationworldmodelsdiffusionhybrid

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that generative AI is no longer only a tool for making images or text but a family of methods now being applied to the core tasks of autonomous driving: building static maps, generating dynamic traffic scenarios, forecasting trajectories, and planning the ego vehicle's motion. It argues that different generative families—autoencoders, GANs, invertible networks, transformers, energy-based models, and diffusion models—have complementary strengths for these tasks, and that hybrid designs combining generative components with classical planners are the most practical route. A sympathetic reader would take the paper's central contention to be this: the main open problems for automotive generative AI are not model quality alone but safety guarantees, interpretability, and the ability to run inside real-time and edge-hardware budgets. The paper also recommends closely following the trend toward LLM-reasoning planners and hybrid latent-space designs while treating benchmark evidence with care.

What carries the argument

The central object is the two-sided map of the autonomous-driving stack: the scene-and-scenario generation side (static map generation, dynamic scenario generation, world models) and the prediction-and-planning side (marginal, conditional, and joint trajectory forecasting; hybrid and end-to-end planning). The load-bearing mechanism is the taxonomy of generative model families—VAEs, GANs, normalizing flows and invertible neural networks, generative transformers, diffusion models, and energy-based models—paired with conditioning and online guidance, which the paper uses to explain why a given method suits a given AD task and where its failure modes (mode collapse, slow sampling, opacity, domain gap) bite.

What would settle it

Run a controlled closed-loop study, in CARLA or a shadow-mode setting, that pits a leading LLM-or-diffusion planner against a simple rule-based planner across out-of-distribution scenarios and reports collision and intervention rates separately from open-loop displacement error; if the generative planner does not beat the rule-based baseline on safety-relevant closed-loop metrics, the paper's recommendation to prioritize generative reasoning planners loses its empirical foundation.

Watch

Extended reading notes

Core claim

This is a survey, and its central claim is organizational: a single generative-model lens can account for both halves of autonomous driving—creating the world the vehicle sees (static scenes, dynamic scenarios, world models) and deciding how the vehicle acts within it (trajectory forecasting, motion planning, end-to-end driving). Within that lens, the paper maps each generative family to the tasks where its properties matter: diffusion models for high-fidelity, diverse scene and trajectory samples; VAEs for compact latent representations; GANs for high fidelity with mode-collapse risks; normalizing flows and invertible networks for exact density modeling; transformers and LLMs for sequential, language-conditioned reasoning; and energy-based models for flexible multimodal scoring. It further claims that conditioning and guidance mechanisms—text prompts, signal temporal logic, cost functions, control barrier functions—are the bridge that turns raw generators into usable driving components. The paper's stated conclusion is that hybrid methods, which keep classical planners and safety filters in the loop while using generative models for proposals, context, and reasoning, are likely to remain competitive, and that the decisive hurdles for deployment are safety and verification, interpretability at scale, and real-time feasibility on automotive hardware.

Load-bearing premise

The survey's forward-looking recommendations assume that current benchmark evidence—especially public leaderboards and open-loop metrics—is a trustworthy measure of real driving competence, even though the paper itself notes that learned planners often fail to outperform simpler methods in closed-loop settings and that closed-loop benchmarks are limited.

Editorial extensions

If this is right

If the survey's map is right, a developer can select a generative family by task: diffusion for diverse scene and trajectory generation, VAEs for compact latent driving representations, autoregressive transformers for language-conditioned reasoning, and classical planners for constraint satisfaction.
Hybrid designs—generative trajectory proposals refined by model predictive control, or LLMs choosing high-level behavior with a rule-based planner as verifier—should keep outperforming purely generative or purely classical alternatives.
LLM-based planners are positioned as a main line of progress for reasoning and interpretability, but only if their real-time and spatial-reasoning gaps are closed.
Closed-loop evaluation, not open-loop imitation error, is the standard on which generative planners must be judged; the paper notes learned planners often fail to beat simpler methods once dynamics and interaction are included.
Better measures of the synthetic-to-real domain gap in scenes and scenarios are needed before generative data can replace real-world training and validation data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The survey's enthusiasm for LLM-reasoning planners rests partly on leaderboard evidence whose validity the paper itself questions elsewhere; a safer reading is that open-loop benchmarks overstate the lead of learned planners until closed-loop results catch up.
Text-conditioned scenario generators suggest a near-term consequence the paper leaves implicit: natural-language scenario specifications could become a practical interface for safety testing, letting engineers generate corner cases without hand-coding them.
If hybrid generative-plus-classical planning becomes the norm, the competitive advantage will likely shift to the safety-filter and verification layer (control barrier functions, reachability analysis), not to the generative backbone itself.
A testable extension would be a standardized closed-loop benchmark that reports generative planners' accident rates separately from average driving scores, since the paper notes even leading algorithms still show measurable accident rates.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Worth a look if you need a map of this area. The paper is a genuinely useful synthesis: it organizes GenAI for AD into scene/scenario generation, prediction, and planning, and the ordering makes sense. The fundamentals section is a decent primer, the discussion of marginal/conditional/joint prediction is clear, and the datasets/simulators table is a practical reference. It also covers hybrid methods (MPC plus diffusion or LLMs) and world models, which is current and not just a list. The writing is above average for a 24-page multi-author survey.

The stress-test concern lands. Section IX-B says GenAI approaches "appear to be overtaking pure RL solutions, as reflected, for instance, in the CARLA leaderboard at the time of writing," with no citation, no date, and no analysis of what that leaderboard measures. Section VII-B, a few pages earlier, says learned planners "often fail to outperform simpler methods in closed-loop settings" and that closed-loop benchmarks are limited. Since CARLA is the closed-loop setting, the recommendation in IX-B leans on evidence the paper itself calls unreliable. That is a genuine internal tension. It is not a fatal flaw—the authors hedge by saying it is "too early to assess long-term impact"—but it is exactly the sort of claim a careful reader will stumble on, and it should be fixed with a citation or softened language.

The other soft spot is the lack of any systematic selection protocol. The paper does not claim to be a systematic review, but without a protocol you cannot tell how representative the coverage is. That is a common limitation of narrative surveys, and it is not disqualifying; just read it as orientation, not as an audit.

Who is this for? Graduate students and engineers entering the GenAI-for-AD space, and researchers who want a quick reference for methods and datasets. Experts will want more critical depth, but they are not the intended audience.

Recommendation: yes, this deserves a serious referee. A referee should ask for the leaderboard claim to be substantiated, for the VII-B/IX-B tension to be resolved, and for a sentence about how papers were selected. I would cite this as an entry point to the field.

Referee Report

3 major / 4 minor

Summary. This manuscript is a survey of generative artificial intelligence (GenAI) methods applied to autonomous driving (AD). It begins with a review of generative model families—normalizing flows and invertible networks, neural ODEs, VAEs, GANs, diffusion models, generative transformers, and energy-based models—and of classical learning strategies such as supervised learning, RL, and imitation learning. It then maps these methods onto the AD stack, covering static map generation, dynamic scenario generation, world models, trajectory prediction, motion planning, and end-to-end driving. The survey also tabulates motion datasets, describes simulators, and concludes with challenges (safety, interpretability, real-time feasibility) and recommendations for model selection, latent-space design, scene generation, planning, and training data. The central claim is that GenAI can enhance multiple AD tasks and that structured guidance on model capabilities and open problems is needed.

Significance. If the synthesis were fully reliable, this survey would be a useful entry point for researchers and practitioners seeking a broad map of generative methods in AD. Its strengths include the breadth of model families covered, the explicit distinction between scenes and scenarios, the inclusion of recent world-model and LLM-based planners, and the concrete discussion of datasets, simulators, and open challenges. The paper is also candid in places, correctly noting that closed-loop evaluation is underdeveloped and that learned planners often fail to beat simpler baselines. However, the survey's forward-looking recommendations currently rest on at least one unsupported and internally contradictory benchmark claim (Section IX-B), and the fundamentals section contains a nontrivial misclassification of model families (Section II.A). Because the value of a survey lies in the trustworthiness of its synthesis, these issues prevent the paper from being accepted as is.

major comments (3)

[Section IX-B and Section VII-B] Section IX-B states: 'GenAI approaches appear to be overtaking pure RL solutions, as reflected, for instance, in the CARLA leaderboard at the time of writing.' This claim is made without a citation, date, or leaderboard snapshot, and it contradicts Section VII-B, which reports that closed-loop benchmarks are limited, that imitation-based planners 'lack the robust generalization of rule-based methods in closed-loop evaluation [259],' and that end-to-end models 'often fail to outperform simpler methods in closed-loop settings [260].' Because the forward-looking recommendation to prioritize LLM-reasoning planners and hybrids rests on this leaderboard evidence, the paper needs either a concrete, dated leaderboard reference with a validity discussion or a substantially softened claim that is consistent with its own closed-loop evidence.
[Section IX-B] The sentence 'recent leading architectures [181] demonstrate that V AEs can generate high-quality images when trained at scale and when their reconstruction loss is combined with GAN-like adversarial losses on patches' misattributes the result. Reference [181] is Rombach et al., 'High-Resolution Image Synthesis with Latent Diffusion Models,' which uses a KL-regularized autoencoder in a latent diffusion framework; it does not demonstrate that a VAE with adversarial patch losses yields state-of-the-art image quality. The claim should either cite the correct source (e.g., a VQGAN-based architecture) or be rephrased to match what [181] actually shows.
[Section II.A] The taxonomy in Section II.A classifies VAEs and EBMs as 'implicit generative models,' but VAEs optimize an explicit likelihood lower bound and EBMs define an explicit unnormalized density; only models like GANs that generate without a tractable density are conventionally called implicit. This is not merely a terminology quibble: the subsequent discussion of capabilities and limitations (e.g., exact likelihood estimation, training stability) depends on the correct category. The authors should either revise the categories or explicitly justify an unconventional definition early in the section.

minor comments (4)

[Sections II.A, IV.A, VII.B] There are several typos and grammar issues, including 'latent space,enabling' in Section II.A, 'VAEss' in Section II.A.d, 'multi-model behaviors' in Section IV.A (should be 'multi-modal'), and 'approachees' in Section VII.B; these should be corrected in a careful proofreading pass.
[Section IX.A] The phrase 'diffusion models coupled with RL frameworks used by GAIA [6]' mischaracterizes GAIA-1, which is an autoregressive transformer-based world model rather than a diffusion model combined with reinforcement learning; please correct or remove this description.
[Section I] The introduction describes the survey as 'more comprehensive' than prior surveys, but no protocol for literature retrieval, inclusion/exclusion criteria, or quality assessment is given; adding a short methodology statement would improve reproducibility and help readers judge the coverage.
[Section VIII, Table I] Table I is introduced with the citation [263], but it aggregates statistics from multiple datasets and sources; please clarify the provenance of each row and, if any entries are time-dependent, provide the retrieval date.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey organizes external literature and its recommendations do not reduce to its own inputs; self-citations are illustrative rather than load-bearing.

full rationale

This paper is a literature review with no derived equations, fitted parameters, or first-principles predictions, so the classical circularity failure modes do not arise. Its structure—mapping generative model families (VAEs, GANs, INNs, GTs, DMs) to AD tasks (map creation, scenario generation, trajectory forecasting, planning)—is an organizational taxonomy whose categories are defined independently of any particular conclusion, not an inference from premises to a target result. The forward-looking recommendations in Section IX-B are opinions grounded in the surveyed literature and external leaderboards; the unsourced CARLA leaderboard statement is an evidence-quality or correctness concern, not a circular one. The authors cite their own prior works (e.g., [62], [177], [198], [256], [296]), but these appear as examples of existing methods or as support for general claims alongside many external references. The only quasi-uniqueness statement, 'To the best of the authors’ knowledge, only one work [177] has applied both concept-based and mechanistic interpretability to AD' (Section IX-A), is presented as a literature-coverage claim rather than as a theorem used to force a choice, and the survey's central content does not depend on it. No step in the paper reduces to its own input by construction, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

This is a literature review. It introduces no fitted parameters, no new mathematical postulates, and no new entities. The only inputs are published papers and datasets, which are handled under axioms and citation_context_check.

assumptions (3)

domain assumption A modular AD stack consisting of perception, prediction, and planning is a valid organizing lens for generative AI in driving.
Section III-A defines the stack and the entire survey is structured around it; if the field's center of gravity shifts to end-to-end monolithic models, this taxonomy may misrepresent the landscape.
domain assumption Cited works are accurately described and their reported results are trustworthy.
The survey's comparative claims, for example that diffusion models provide high fidelity and diversity in Section II-A-e, depend on the abstracts and results of cited papers; no independent replication is performed.
domain assumption The classification of generative models into reversible, implicit, and transformer-based families is well-defined and covers the relevant space.
Section II-A groups VAEs, GANs, DMs, and EBMs as implicit models, and NFs, INNs, and NODEs as reversible models; some newer models do not fit cleanly, but the survey proceeds without discussing this boundary.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Generative AI for Autonomous Driving: A Review." pith.science (2026). https://pith.science/paper/JXJYNMSM

@misc{pith2026250515863,
  author       = {Pith},
  title        = {Pith review of: Generative AI for Autonomous Driving: A Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JXJYNMSM}},
  note         = {Machine review of arXiv:2505.15863}
}

read the original abstract

Generative AI (GenAI) is rapidly advancing the field of Autonomous Driving (AD), extending beyond traditional applications in text, image, and video generation. We explore how generative models can enhance automotive tasks, such as static map creation, dynamic scenario generation, trajectory forecasting, and vehicle motion planning. By examining multiple generative approaches ranging from Variational Autoencoder (VAEs) over Generative Adversarial Networks (GANs) and Invertible Neural Networks (INNs) to Generative Transformers (GTs) and Diffusion Models (DMs), we highlight and compare their capabilities and limitations for AD-specific applications. Additionally, we discuss hybrid methods integrating conventional techniques with generative approaches, and emphasize their improved adaptability and robustness. We also identify relevant datasets and outline open research questions to guide future developments in GenAI. Finally, we discuss three core challenges: safety, interpretability, and realtime capabilities, and present recommendations for image generation, dynamic scenario generation, and planning.

Figures

Figures reproduced from arXiv: 2505.15863 by the authors.

**Figure 1.** The Autonomous Driving (AD) stack. neighboring agents affect each other. To this end, joint motion prediction is necessary, where the interaction among multimodal proposed trajectories of all the agents in a scene is modeled for future time steps. Several approaches have been proposed in recent years to model the interaction among future time steps for joint motion prediction. The work by Park et al. [111] models s… view at source ↗

**Figure 2.** Comparison of prediction types in a traffic intersection scenario. Blue and orange colors represent different prediction [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 4.** Demonstration of text-prompt to scenario generation: [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

Figures from the paper (1 more)

**Figure 3.** Figure 3: Demonstration of scenario generation starting from an [PITH_FULL_IMAGE:figures/full_fig_p008_3.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Extended Field of View Analysis for VideoGAN-based Trajectory Generation
cs.CV 2026-08 conditional novelty 5.0 of 10

A video GAN trained on semantic top-down traffic videos generates 15–25 m field-of-view scenes whose speed, acceleration, spacing, and time-to-collision statistics resemble real Waymo data, with inference below 20 ms.
Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey
cs.RO 2025-09 conditional novelty 4.0 of 10

A structured survey of LLM-based trajectory prediction methods, organized into trajectory-language mapping, multimodal fusion, and constraint-based reasoning, with benchmarks, metrics, and future directions.

Reference graph

Works this paper leans on

300 extracted references · 14 canonical work pages · cited by 2 Pith papers

[259]

Parting with Misconceptions about Learning-based Vehicle Motion Planning,

D. Dauner, M. Hallgarten, A. Geiger, and K. Chitta, “Parting with Misconceptions about Learning-based Vehicle Motion Planning,” 2023, arXiv:2306.07962

arXiv 2023
[260]

NA VSIM: Data- Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking,

D. Dauner, M. Hallgarten, T. Li, et al. , “NA VSIM: Data- Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking,” 2024, arXiv:2406.15349

arXiv 2024
[181]

High-Resolution Image Synthesis with Latent Dif- fusion Models,

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis with Latent Dif- fusion Models,” 2022, arXiv:2112.10752

arXiv 2022
[1]

The cityscapes dataset for semantic urban scene understanding,

M. Cordts, M. Omran, S. Ramos, et al. , “The cityscapes dataset for semantic urban scene understanding,” in Proc. IEEE Conf. on Comput. Vis. Pattern Recognit. , 2016

2016
[2]

Vision meets robotics: The kitti dataset,

A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” Int. J. Robot. Res. , 2013

2013
[3]

Bdd100k: A diverse driving video database with scalable annotation tooling,

F. Yu, W. Xian, Y . Chen, et al. , “Bdd100k: A diverse driving video database with scalable annotation tooling,” 2018, arXiv:1805.04687

arXiv 2018
[4]

Generalization by adaptation: Diffusion-based domain extension for domain-generalized semantic segmentation,

J. Niemeijer, M. Schwonberg, J.-A. Termöhlen, N. M. Schmidt, and T. Fingscheidt, “Generalization by adaptation: Diffusion-based domain extension for domain-generalized semantic segmentation,” in IEEE / CVF Comput. Vis. Pattern Recognit. Conf. Workshop, 2024. PREPRINT 17

2024
[5]

Survey on unsupervised domain adaptation for semantic segmentation for visual perception in automated driving,

M. Schwonberg, J. Niemeijer, J.-A. Termöhlen, N. M. Schmidt, H. Gottschalk, T. Fingscheidt, et al. , “Survey on unsupervised domain adaptation for semantic segmentation for visual perception in automated driving,” IEEE Access , 2023

2023

Show all 300 references

[6]

GAIA-1: A Gen- erative World Model for Autonomous Driving,

A. Hu, L. Russell, H. Yeo, et al. , “GAIA-1: A Gen- erative World Model for Autonomous Driving,” 2023, arXiv:2309.17080

2023 arXiv
[7]

Adding condi- tional control to text-to-image diffusion models,

L. Zhang, A. Rao, and M. Agrawala, “Adding condi- tional control to text-to-image diffusion models,” in Proc. IEEE/CVF Int. Conf. on Comput. Vis. , 2023

2023
[8]

Semi- Supervised Domain Adaptation with CycleGAN Guided by Downstream Task Awareness.,

A. Mütze, M. Rottmann, and H. Gottschalk, “Semi- Supervised Domain Adaptation with CycleGAN Guided by Downstream Task Awareness.,” inInt. Conf. on Comput. Vis. Theory Appl., 2023

2023
[9]

A Survey for Foundation Models in Autonomous Driving,

H. Gao, Z. Wang, Y . Li, K. Long, M. Yang, and Y . Shen, “A Survey for Foundation Models in Autonomous Driving,” 2024, arXiv:2402.01105

2024 arXiv
[10]

Prospective Role of Foun- dation Models in Advancing Autonomous Vehicles,

J. Wu, B. Gao, J. Gao, et al. , “Prospective Role of Foun- dation Models in Advancing Autonomous Vehicles,” 2024, arXiv:2405.02288

2024 arXiv
[11]

LLM4Drive: A Survey of Large Language Models for Autonomous Driving,

Z. Yang, X. Jia, H. Li, and J. Yan, “LLM4Drive: A Survey of Large Language Models for Autonomous Driving,” 2024, arXiv:2311.01043

2024 arXiv
[12]

Large Language Models for Human-like Autonomous Driving: A Survey,

Y . Li, K. Katsumata, E. Javanmardi, and M. Tsukada, “Large Language Models for Human-like Autonomous Driving: A Survey,” 2024, arXiv:2407.19280

2024 arXiv
[13]

A Survey on Safety-Critical Driving Scenario Generation—A Methodological Perspective,

W. Ding, C. Xu, M. Arief, H. Lin, B. Li, and D. Zhao, “A Survey on Safety-Critical Driving Scenario Generation—A Methodological Perspective,” IEEE Trans. on Intell. Transp. Syst., 2023

2023
[14]

A Survey of Scenario Generation for Automated Vehicle Testing and Validation,

Z. Wang, J. Ma, and E. M.-K. Lai, “A Survey of Scenario Generation for Automated Vehicle Testing and Validation,” Future Internet, 2024

2024
[15]

A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective,

H. Yan and Y . Li, “A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective,” 2024, arXiv:2312.08248

2024 arXiv
[16]

Distances Between Probability Distributions of Different Dimensions,

Y . Cai and L.-H. Lim, “Distances Between Probability Distributions of Different Dimensions,” IEEE Trans. on Inf. Theory, 2022

2022
[17]

Normalizing Flows: An Introduction and Review of Current Methods,

I. Kobyzev, S. J. Prince, and M. A. Brubaker, “Normalizing Flows: An Introduction and Review of Current Methods,” IEEE Trans. on Pattern Anal. Mach. Intell. , 2021

2021
[18]

Variational Inference with Normalizing Flows,

D. Rezende and S. Mohamed, “Variational Inference with Normalizing Flows,” in Proc. 32nd Int. Conf. on Mach. Learn., 2015

2015
[19]

Deep Generative Modelling: A Comparative Review of V AEs, GANs, Normalizing Flows, Energy-Based and Au- toregressive Models,

S. Bond-Taylor, A. Leach, Y . Long, and C. G. Willcocks, “Deep Generative Modelling: A Comparative Review of V AEs, GANs, Normalizing Flows, Energy-Based and Au- toregressive Models,” IEEE Trans. on Pattern Anal. Mach. Intell., 2022

2022
[20]

Universal Approximation Property of Invertible Neural Networks,

I. Ishikawa, T. Teshima, K. Tojo, K. Oono, M. Ikeda, and M. Sugiyama, “Universal Approximation Property of Invertible Neural Networks,” J. Mach. Learn. Res. , 2023

2023
[21]

Deep Residual Learn- ing for Image Recognition,

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learn- ing for Image Recognition,” in 2016 IEEE Conf. Comput. Vis. Pattern Recognit., 2016

2016
[22]

Neural Ordinary Differential Equations,

T. Q. Chen, Y . Rubanova, J. Bettencourt, and D. Duvenaud, “Neural Ordinary Differential Equations,” in Annu. Conf. on Neural Inf. Process. Syst. , 2018

2018
[23]

Flow Matching for Generative Modeling,

Y . Lipman, R. T. Q. Chen, H. Ben-Hamu, M. Nickel, and M. Le, “Flow Matching for Generative Modeling,” 2023, arXiv:2210.02747

2023 arXiv
[24]

Auto-Encoding Variational Bayes,

D. P. Kingma and M. Welling, “Auto-Encoding Variational Bayes,” 2022, arXiv:1312.6114

2022 arXiv
[25]

Genera- tive adversarial networks,

I. Goodfellow, J. Pouget-Abadie, M. Mirza, et al., “Genera- tive adversarial networks,” Commun. ACM, 2020

2020
[26]

A Style-Based Generator Architecture for Generative Adversarial Networks,

T. Karras, S. Laine, and T. Aila, “A Style-Based Generator Architecture for Generative Adversarial Networks,” IEEE Trans. on Pattern Anal. Mach. Intell. , 2021

2021
[27]

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions,

D. Saxena and J. Cao, “Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions,” ACM Comput. Surv. , 2022

2022
[28]

Deep Unsupervised Learning using Nonequilib- rium Thermodynamics,

J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep Unsupervised Learning using Nonequilib- rium Thermodynamics,” in Proc. 32nd Int. Conf. on Mach. Learn., 2015

2015
[29]

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise,

A. Bansal, E. Borgnia, H.-M. Chu, et al. , “Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise,” in Neural Inf. Process. Syst. , 2023

2023
[30]

Diffusion Models in Vision: A Survey,

F.-A. Croitoru, V . Hondru, R. T. Ionescu, and M. Shah, “Diffusion Models in Vision: A Survey,” IEEE Trans. on Pattern Anal. Mach. Intell. , 2023

2023
[31]

Attention is All you Need,

A. Vaswani, N. Shazeer, N. Parmar, et al., “Attention is All you Need,” in Adv. Neural Inf. Process. Syst. , 2017

2017
[32]

A tutorial on energy-based learning,

Y . LeCun, S. Chopra, R. Hadsell, M. Ranzato, and F.-J. Huang, “A tutorial on energy-based learning,” in Predict. Struct. Data, 2006

2006
[33]

Training products of experts by minimizing contrastive divergence,

G. E. Hinton, “Training products of experts by minimizing contrastive divergence,” Neural Comput., 2002

2002
[34]

Estimation of non-normalized statistical models by score matching.,

A. Hyvärinen and P. Dayan, “Estimation of non-normalized statistical models by score matching.,” J. Mach. Learn. Res., 2005

2005
[35]

MCMC Using Hamiltonian Dynamics,

R. M. Neal, “MCMC Using Hamiltonian Dynamics,” in Handb. Markov Chain Monte Carlo , Section: 5, 2011

2011
[36]

Your classifier is secretly an energy based model and you should treat it like one,

D. Duvenaud, J. Wang, J. Jacobsen, K. Swersky, M. Norouzi, and W. Grathwohl, “Your classifier is secretly an energy based model and you should treat it like one,” in Int. Conf. on Learn. Represent. , 2020

2020
[37]

Energy-Based Diffusion Language Models for Text Generation,

M. Xu, T. Geffner, K. Kreis, et al. , “Energy-Based Diffusion Language Models for Text Generation,” 2024, arXiv.2410.21357

2024 arXiv
[38]

Training energy-based normalizing flow with score- matching objectives,

C.-H. Chao, W.-F. Sun, Y .-C. Hsu, Z. Kira, and C.-Y . Lee, “Training energy-based normalizing flow with score- matching objectives,” Adv. Neural Inf. Process. Syst. , 2024

2024
[39]

Energy transformer,

B. Hoover, Y . Liang, B. Pham, et al., “Energy transformer,” Adv. Neural Inf. Process. Syst. , 2024

2024
[40]

Hitchhiker’s guide on Energy-Based Mod- els: A comprehensive review on the relation with other generative models, sampling and statistical physics,

D. Carbone, “Hitchhiker’s guide on Energy-Based Mod- els: A comprehensive review on the relation with other generative models, sampling and statistical physics,” 2024, arXiv:2406.13661

2024 arXiv
[41]

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, Second. The MIT Press, 2015

2015
[42]

A Com- prehensive Survey of Reinforcement Learning: From Algo- rithms to Practical Challenges,

M. Ghasemi, A. H. Moosavi, and D. Ebrahimi, “A Com- prehensive Survey of Reinforcement Learning: From Algo- rithms to Practical Challenges,” 2025, arXiv:2411.18892

2025 arXiv
[43]

Model-Based or Model-Free, a Review of Ap- proaches in Reinforcement Learning,

Q. Huang, “Model-Based or Model-Free, a Review of Ap- proaches in Reinforcement Learning,” in 2020 Int. Conf. on Comput. Data Sci. , 2020

2020
[44]

A Survey of Imitation Learning: Algorithms, Recent Develop- ments, and Challenges,

M. Zare, P. M. Kebria, A. Khosravi, and S. Nahavandi, “A Survey of Imitation Learning: Algorithms, Recent Develop- ments, and Challenges,” IEEE Trans. on Cybern. , 2023

2023
[45]

Explor- ing the Limitations of Behavior Cloning for Autonomous Driving,

F. Codevilla, E. Santana, A. Lopez, and A. Gaidon, “Explor- ing the Limitations of Behavior Cloning for Autonomous Driving,” in 2019 IEEE/CVF Int. Conf. on Comput. Vis. , 2019

2019
[46]

A survey of inverse reinforce- ment learning: Challenges, methods and progress,

S. Arora and P. Doshi, “A survey of inverse reinforce- ment learning: Challenges, methods and progress,” 2021, arXiv:1806.06877

2021 arXiv
[47]

Human-inspired autonomous driving: A survey,

A. Plebe, H. Svensson, S. Mahmoud, and M. D. Lio, “Human-inspired autonomous driving: A survey,” Cogn. Syst. Res., 2024

2024
[48]

Image Inpainting via Conditional Texture and Structure Dual Generation,

X. Guo, H. Yang, and D. Huang, “Image Inpainting via Conditional Texture and Structure Dual Generation,” 2024, arXiv:2108.09760. PREPRINT 18

2024 arXiv
[49]

Vista: A framework for virtual scenario-based testing of autonomous vehicles,

A. Piazzoni, J. Cherian, M. Azhar, J. Y . Yap, J. L. W. Shung, and R. Vijay, “Vista: A framework for virtual scenario-based testing of autonomous vehicles,” in 2021 IEEE Int. Conf. on Artif. Intell. Test., 2021

2021
[50]

Generalized predictive model for autonomous driving,

J. Yang, S. Gao, Y . Qiu, et al. , “Generalized predictive model for autonomous driving,” in Proc. IEEE/CVF Conf. on Comput. Vis. Pattern Recognit. , 2024

2024
[51]

Guided conditional diffusion for controllable traffic simulation,

Z. Zhong, D. Rempe, D. Xu, et al. , “Guided conditional diffusion for controllable traffic simulation,” in 2023 IEEE Int. Conf. on Robotics Autom. , 2023

2023
[52]

Motiondiffuser: Controllable multi-agent motion prediction using diffusion,

C. Jiang, A. Cornman, C. Park, B. Sapp, Y . Zhou, D. Anguelov, et al. , “Motiondiffuser: Controllable multi-agent motion prediction using diffusion,” in Proc. IEEE/CVF Conf. on Comput. Vis. Pattern Recognit. , 2023

2023
[53]

GenAD: Generative End-to-End Autonomous Driving,

W. Zheng, R. Song, X. Guo, C. Zhang, and L. Chen, “GenAD: Generative End-to-End Autonomous Driving,” in Eur. Conf. on Comput. Vis. 2024 , 2025

2024
[54]

Diffscene: Diffusion-based safety-critical scenario genera- tion for autonomous vehicles,

C. Xu, D. Zhao, A. Sangiovanni-Vincentelli, and B. Li, “Diffscene: Diffusion-based safety-critical scenario genera- tion for autonomous vehicles,” in The Second. Workshop on New Front. Adversarial Mach. Learn. , 2023

2023
[55]

Versatile Scene-Consistent Traffic Sce- nario Generation as Optimization with Diffusion,

Z. Huang, Z. Zhang, A. Vaidya, Y . Chen, C. Lv, and J. F. Fisac, “Versatile Scene-Consistent Traffic Sce- nario Generation as Optimization with Diffusion,” 2024, arXiv:2404.02524

2024
[56]

DriveDreamer-2: LLM- Enhanced World Models for Diverse Driving Video Gener- ation,

G. Zhao, X. Wang, Z. Zhu, et al., “DriveDreamer-2: LLM- Enhanced World Models for Diverse Driving Video Gener- ation,” 2024, arXiv:2403.06845

2024 arXiv
[57]

GANs Condi- tioning Methods: A Survey,

A. Bourou, A. Genovesio, and V . Mezger, “GANs Condi- tioning Methods: A Survey,” 2024, arXiv: 2408.15640

2024 arXiv
[58]

Learning conditional variational au- toencoders with missing covariates,

S. Ramchandran, G. Tikhonov, O. Lönnroth, P. Tiikkainen, and H. Lähdesmäki, “Learning conditional variational au- toencoders with missing covariates,”Pattern Recognit., 2024

2024
[59]

Learning Likelihoods with Conditional Normal- izing Flows,

C. Winkler, D. E. Worrall, E. Hoogeboom, and M. Welling, “Learning Likelihoods with Conditional Normal- izing Flows,” 2019, arXiv: 1912.00042

2019 arXiv
[60]

MotionLM: Multi-Agent Motion Forecasting as Language Modeling,

A. Seff, B. Cera, D. Chen, et al., “MotionLM: Multi-Agent Motion Forecasting as Language Modeling,” in Int. Conf. on Comput. Vis., 2023

2023
[61]

Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods,

E. Hüllermeier and W. Waegeman, “Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods,” Mach. Learn. Springer , 2021

2021
[62]

Motion Planning under Uncertainty: Integrating Learning- Based Multi-Modal Predictors into Branch Model Predictive Control,

M.-K. Bouzidi, B. Derajic, D. Goehring, and J. Reichardt, “Motion Planning under Uncertainty: Integrating Learning- Based Multi-Modal Predictors into Branch Model Predictive Control,” 2024, arXiv: 2405.03470

2024 arXiv
[63]

RACP: Risk-Aware Contingency Planning with Multi- Modal Predictions,

K. Mustafa, D. Jarne Ornia, J. Kober, and J. Alonso-Mora, “RACP: Risk-Aware Contingency Planning with Multi- Modal Predictions,” IEEE Intell. Veh. Symp. , 2024

2024
[64]

A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification,

A. N. Angelopoulos and S. Bates, “A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification,” 2021, arXiv: 2107.07511

2021 arXiv
[65]

Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift,

F. Seligmann, P. Becker, M. V olpp, and G. Neumann, “Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift,” in Neural Inf. Process. Syst. , 2023

2023
[66]

Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks,

B. Mucsányi, M. Kirchhof, and S. J. Oh, “Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks,” 2024, arXiv: 2402.19460

2024 arXiv
[67]

Dropout as a Bayesian Approxi- mation: Representing Model Uncertainty in Deep Learning,

Y . Gal and Z. Ghahramani, “Dropout as a Bayesian Approxi- mation: Representing Model Uncertainty in Deep Learning,” in 33nd Int. Conf. on Mach. Learn. , 2016

2016
[68]

A Simple Baseline for Bayesian Uncertainty in Deep Learning,

W. J. Maddox, P. Izmailov, T. Garipov, D. P. Vetrov, and A. G. Wilson, “A Simple Baseline for Bayesian Uncertainty in Deep Learning,” in Neural Inf. Process. Syst. , 2019

2019
[69]

Laplace Redux - Effortless Bayesian Deep Learning,

E. A. Daxberger, A. Kristiadi, A. Immer, R. Eschenhagen, M. Bauer, and P. Hennig, “Laplace Redux - Effortless Bayesian Deep Learning,” in Annu. Conf. on Neural Inf. Process. Syst., 2021

2021
[70]

Generalized Vari- ational Continual Learning,

N. Loo, S. Swaroop, and R. E. Turner, “Generalized Vari- ational Continual Learning,” in Int. Conf. on Learn. Repre- sent., 2021

2021
[71]

Training, Ar- chitecture, and Prior for Deterministic Uncertainty Methods,

B. Charpentier, C. Zhang, and S. Günnemann, “Training, Ar- chitecture, and Prior for Deterministic Uncertainty Methods,” Int. Conf. on Learn. Represent. 2023 Workshop on Pitfalls limited data computation for Trust. ML , 2023

2023
[72]

On the Practicality of Deterministic Epistemic Uncertainty,

J. Postels, M. Segù, T. Sun, et al. , “On the Practicality of Deterministic Epistemic Uncertainty,” in Proc. 39th Int. Conf. on Mach. Learn. 2022 , 2022

2022
[73]

Simple and principled uncertainty estimation with deterministic deep learning via distance awareness,

J. Liu, Z. Lin, S. Padhy, D. Tran, T. Bedrax Weiss, and B. Lakshminarayanan, “Simple and principled uncertainty estimation with deterministic deep learning via distance awareness,” Adv. Neural Inf. Process. Syst. 33: Annu. Conf. on Neural Inf. Process. Syst. , 2020

2020
[74]

Solving Bayesian Inverse Problems via Variational Autoen- coders,

H. Goh, S. Sheriffdeen, J. Wittmer, and T. Bui-Thanh, “Solving Bayesian Inverse Problems via Variational Autoen- coders,” in Math. Sci. Mach. Learn. , 2021

2021
[75]

UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs,

P. Oberdiek, G. A. Fink, and M. Rottmann, “UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs,” in Annu. Conf. on Neural Inf. Process. Syst. , 2022

2022
[76]

Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model,

J. Sun, Y . Jiang, J. Qiu, P. Nobel, M. J. Kochenderfer, and M. Schwager, “Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model,” in Annu. Conf. on Neural Inf. Process. Syst. , 2023

2023
[77]

Uncertainty Quantifi- cation with Generative Models,

V . Böhm, F. Lanusse, and U. Seljak, “Uncertainty Quantifi- cation with Generative Models,” 2019, arXiv: 1910.10046

2019 arXiv
[78]

DDM- Lag : A Diffusion-based Decision-making Model for Au- tonomous Vehicles with Lagrangian Safety Enhancement,

J. Liu, P. Hang, X. Zhao, J. Wang, and J. Sun, “DDM- Lag : A Diffusion-based Decision-making Model for Au- tonomous Vehicles with Lagrangian Safety Enhancement,” 2024, arXiv:2401.03629

2024 arXiv
[79]

Conformal Prediction for Natural Language Processing: A Survey,

M. M. Campos, A. Farinhas, C. Zerva, M. A. T. Figueiredo, and A. F. T. Martins, “Conformal Prediction for Natural Language Processing: A Survey,” 2024, arXiv: 2405.01976

2024 arXiv
[80]

Adaptive Uncertainty Quantification for Generative AI,

J. Kim, S. O’Hagan, and V . Rocková, “Adaptive Uncertainty Quantification for Generative AI,” 2024, arXiv: 2408.08990

2024 arXiv
[81]

Uncertainty Quantifi- cation for In-Context Learning of Large Language Models,

C. Ling, X. Zhao, X. Zhang, et al. , “Uncertainty Quantifi- cation for In-Context Learning of Large Language Models,” in Proc. 2024 Conf. North Am. Chapter Assoc. for Comput. Linguist. Hum. Lang. Technol. , 2024

2024
[82]

Mp3: A unified model to map, perceive, predict and plan,

S. Casas, A. Sadat, and R. Urtasun, “Mp3: A unified model to map, perceive, predict and plan,” inProc. IEEE/CVF Conf. on Comput. Vis. Pattern Recognit. , 2021

2021
[83]

St- p3: End-to-end vision-based autonomous driving via spatial- temporal feature learning,

S. Hu, L. Chen, P. Wu, H. Li, J. Yan, and D. Tao, “St- p3: End-to-end vision-based autonomous driving via spatial- temporal feature learning,” in Eur. Conf. on Comput. Vis. , 2022

2022
[84]

TransFusion: Robust LiDAR- Camera Fusion for 3D Object Detection with Transformers,

X. Bai, Z. Hu, X. Zhu, et al., “TransFusion: Robust LiDAR- Camera Fusion for 3D Object Detection with Transformers,” 2022, arXiv:2203.11496

2022 arXiv
[85]

Radar-Camera Fusion for Object Detection and Semantic Segmentation in Au- tonomous Driving: A Comprehensive Review,

S. Yao, R. Guan, X. Huang, et al. , “Radar-Camera Fusion for Object Detection and Semantic Segmentation in Au- tonomous Driving: A Comprehensive Review,” IEEE Trans. on Intell. Veh., 2024

2024
[86]

Integration of GPS and dead-reckoning navi- gation systems,

W.-W. Kao, “Integration of GPS and dead-reckoning navi- gation systems,” in Veh. Navig. Inf. Syst. Conf. 1991 , 1991

1991
[87]

A Survey of Autonomous Driving: Common Practices and Emerging Technologies,

E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda, “A Survey of Autonomous Driving: Common Practices and Emerging Technologies,” IEEE Access, 2020

2020
[88]

Early vs Late Fusion in Multimodal Convolutional Neural Networks,

K. Gadzicki, R. Khamsehashari, and C. Zetzsche, “Early vs Late Fusion in Multimodal Convolutional Neural Networks,” in IEEE 23rd Int. Conf. on Inf. Fusion , 2020

2020
[89]

Multimodal End-to-End Autonomous Driv- ing,

Y . Xiao, F. Codevilla, A. Gurram, O. Urfalioglu, and A. M. Lopez, “Multimodal End-to-End Autonomous Driv- ing,” IEEE Trans. on Intell. Transp. Syst. , 2022

2022
[90]

A review of high- definition map creation methods for autonomous driving,

Z. Bao, S. Hossain, H. Lang, and X. Lin, “A review of high- definition map creation methods for autonomous driving,” Eng. Appl. Artif. Intell. , 2023. PREPRINT 19

2023
[91]

SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Genera- tion,

H. Dong, W. Gu, X. Zhang, et al., “SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Genera- tion,” in 2024 IEEE Int. Conf. on Robotics Autom. , 2024

2024
[92]

HDMapNet: An Online HD Map Construction and Evaluation Framework,

Q. Li, Y . Wang, Y . Wang, and H. Zhao, “HDMapNet: An Online HD Map Construction and Evaluation Framework,” in 2022 Int. Conf. on Robotics Autom. , 2022

2022
[93]

MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction,

B. Liao, S. Chen, X. Wang, et al. , “MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction,” 2023, arXiv:2208.14437

2023 arXiv
[94]

V AD: Vectorized Scene Representation for Efficient Autonomous Driving,

B. Jiang, S. Chen, Q. Xu, et al. , “V AD: Vectorized Scene Representation for Efficient Autonomous Driving,” 2023, arXiv:2303.12077

2023 arXiv
[95]

Vec- torMapNet: End-to-end Vectorized HD Map Learning,

Y . Liu, T. Yuan, Y . Wang, Y . Wang, and H. Zhao, “Vec- torMapNet: End-to-end Vectorized HD Map Learning,” 2022, arXiv:2206.08920

2022 arXiv
[96]

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation,

J. Gao, C. Sun, H. Zhao, et al. , “VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation,” 2020, arXiv:2005.04259

2020 arXiv
[97]

Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving,

X. Tian, T. Jiang, L. Yun, et al., “Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving,” 2023, arXiv:2304.14365

2023 arXiv
[98]

SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving,

Y . Wei, L. Zhao, W. Zheng, Z. Zhu, J. Zhou, and J. Lu, “SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving,” 2023, arXiv:2303.09551

2023 arXiv
[99]

Delving Into the Devils of Bird’s-Eye-View Perception: A Review, Evaluation and Recipe,

H. Li, C. Sima, J. Dai, et al. , “Delving Into the Devils of Bird’s-Eye-View Perception: A Review, Evaluation and Recipe,” IEEE Trans. on Pattern Anal. Mach. Intell. , 2024

2024
[100]

Language Prompt for Autonomous Driving,

D. Wu, W. Han, T. Wang, Y . Liu, X. Zhang, and J. Shen, “Language Prompt for Autonomous Driving,” in Annu. Conf. on Artif. Intell. 2025 , 2023

2025
[101]

A Survey on Multimodal Large Language Models for Autonomous Driving,

C. Cui, Y . Ma, X. Cao, et al. , “A Survey on Multimodal Large Language Models for Autonomous Driving,” 2023, arXiv:2311.12320

2023 arXiv
[102]

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,

A. Dosovitskiy, L. Beyer, A. Kolesnikov, et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” 2021, arXiv:2010.11929

2021 arXiv
[103]

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation,

C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation,” 2017, arXiv:1612.00593

2017 arXiv
[104]

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models,

J. Li, D. Li, S. Savarese, and S. Hoi, “BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models,” 2023, arXiv:2301.12597

2023 arXiv
[105]

MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction,

Y . Chai, B. Sapp, M. Bansal, and D. Anguelov, “MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction,” in Proc. Conf. on Robot Learn. , 2020

2020
[106]

Scene Trans- former: A unified architecture for predicting multiple agent trajectories,

J. Ngiam, B. Caine, V . Vasudevan, et al. , “Scene Trans- former: A unified architecture for predicting multiple agent trajectories,” 2021, arXiv:2106.08417v3

2021 arXiv
[107]

Context-Aware Scene Prediction Network (CASPNet),

M. Schäfer, K. Zhao, M. Bühren, and A. Kummert, “Context-Aware Scene Prediction Network (CASPNet),” in 2022 IEEE 25th Int. Conf. on Intell. Transp. Syst. , 2022

2022
[108]

Query- Centric Trajectory Prediction,

Z. Zhou, J. Wang, Y .-H. Li, and Y .-K. Huang, “Query- Centric Trajectory Prediction,” in Proc. IEEE/CVF Conf. on Comput. Vis. Pattern Recognit. , 2023

2023
[109]

M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction,

Q. Sun, X. Huang, J. Gu, B. C. Williams, and H. Zhao, “M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction,” inProc. IEEE/CVF Conf. on Comput. Vis. Pattern Recognit., 2022

2022
[110]

Identifying Driver Interactions via Conditional Behavior Prediction,

E. Tolstaya, R. Mahjourian, C. Downey, B. Vadarajan, B. Sapp, and D. Anguelov, “Identifying Driver Interactions via Conditional Behavior Prediction,” in 2021 IEEE Int. Conf. on Robotics Autom. , 2021

2021
[111]

Leveraging Future Relationship Reasoning for Vehicle Tra- jectory Prediction,

D. Park, H. Ryu, Y . Yang, J. Cho, J. Kim, and K. J. Yoon, “Leveraging Future Relationship Reasoning for Vehicle Tra- jectory Prediction,” in Int. Conf. on Learn. Represent., 2023

2023
[112]

JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving,

W. Luo, C. Park, A. Cornman, B. Sapp, and D. Anguelov, “JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving,” in Proc. The 6th Conf. on Robot Learn. , 2023

2023
[113]

HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction,

Z. Zhou, L. Ye, J. Wang, K. Wu, and K. Lu, “HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction,” in Proc. IEEE/CVF Conf. on Comput. Vis. Pattern Recognit., 2022

2022
[114]

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting,

S. Casas, C. Gulino, S. Suo, K. Luo, R. Liao, and R. Ur- tasun, “Implicit Latent Variable Model for Scene-Consistent Motion Forecasting,” in Eur. Conf. on Comput. Vis. , 2020

2020
[115]

LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving,

A. Cui, S. Casas, A. Sadat, R. Liao, and R. Urtasun, “LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving,” in Proc. IEEE/CVF Int. Conf. on Comput. Vis., 2021

2021
[116]

Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction,

R. Girgis, F. Golemo, F. Codevilla, et al. , “Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction,” 2022, arXiv:2104.00563

2022 arXiv
[117]

Defining and Substantiating the Terms Scene, Situation, and Scenario for Automated Driving,

S. Ulbrich, T. Menzel, A. Reschka, F. Schuldt, and M. Mau- rer, “Defining and Substantiating the Terms Scene, Situation, and Scenario for Automated Driving,” in IEEE Int. Conf. on Intell. Transp. Syst. , 2015

2015
[118]

High Definition Map for Automated Driving: Overview and Analysis,

R. Liu, J. Wang, and B. Zhang, “High Definition Map for Automated Driving: Overview and Analysis,” J. Navig., 2020

2020
[119]

High- Definition Maps: Comprehensive Survey, Challenges, and Future Perspectives,

G. Elghazaly, R. Frank, S. Harvey, and S. Safko, “High- Definition Maps: Comprehensive Survey, Challenges, and Future Perspectives,” IEEE Open J. Intell. Transp. Syst. , 2023

2023
[120]

A robust pose graph approach for city scale LiDAR mapping,

S. Yang, X. Zhu, X. Nian, L. Feng, X. Qu, and T. Ma, “A robust pose graph approach for city scale LiDAR mapping,” in 2018 IEEE/RSJ Int. Conf. on Intell. Robots Syst. , 2018

2018
[121]

High-Definition Maps Construction Based on Visual Sensor: A Comprehensive Survey,

X. Tang, K. Jiang, M. Yang, et al. , “High-Definition Maps Construction Based on Visual Sensor: A Comprehensive Survey,” IEEE Trans. on Intell. Veh. , 2023

2023
[122]

A Unified Multi-Frame Strategy for Au- tonomous Vehicle Perception and Localization Using Radar, Camera, LiDAR, and HD Map Fusion,

A. R. Alghooneh, “A Unified Multi-Frame Strategy for Au- tonomous Vehicle Perception and Localization Using Radar, Camera, LiDAR, and HD Map Fusion,” Ph.D. dissertation, University of Waterloo, Waterloo, Belgium, 2024

2024
[123]

Creating Semantic HD Maps From Aerial Imagery and Aggregated Vehicle Telemetry for Autonomous Vehicles,

Y . Wei, F. Mahnaz, O. Bulan, Y . Mengistu, S. Mahesh, and M. A. Losh, “Creating Semantic HD Maps From Aerial Imagery and Aggregated Vehicle Telemetry for Autonomous Vehicles,” IEEE Trans. on Intell. Transp. Syst. , 2022

2022
[124]

Automatic Building and Labeling of HD Maps with Deep Learning,

M. Elhousni, Y . Lyu, Z. Zhang, and X. Huang, “Automatic Building and Labeling of HD Maps with Deep Learning,” Proc. AAAI Conf. on Artif. Intell. , 2020

2020
[125]

Online Map Vectorization for Autonomous Driving: A Rasterization Perspective,

G. Zhang, J. Lin, S. Wu, et al., “Online Map Vectorization for Autonomous Driving: A Rasterization Perspective,” Adv. Neural Inf. Process. Syst. , 2023

2023
[126]

DeepRoadMapper: Extracting Road Topology From Aerial Images,

G. Mattyus, W. Luo, and R. Urtasun, “DeepRoadMapper: Extracting Road Topology From Aerial Images,” inInt. Conf. on Comput. Vis. , 2017

2017
[127]

Topological Map Extraction From Overhead Images,

Z. Li, J. D. Wegner, and A. Lucchi, “Topological Map Extraction From Overhead Images,” in Proc. IEEE/CVF Int. Conf. on Comput. Vis. , 2019

2019
[128]

Neural Turtle Graphics for Modeling City Road Layouts,

H. Chu, D. Li, D. Acuna, et al. , “Neural Turtle Graphics for Modeling City Road Layouts,” in Proc. IEEE/CVF Int. Conf. on Comput. Vis. , 2019

2019
[129]

HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps,

L. Mi, H. Zhao, C. Nash, et al., “HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps,” 2021, arXiv:2106.14880

2021 arXiv
[130]

SLEDGE: Synthesiz- ing Driving Environments with Generative Models and Rule- Based Traffic,

K. Chitta, D. Dauner, and A. Geiger, “SLEDGE: Synthesiz- ing Driving Environments with Generative Models and Rule- Based Traffic,” in Eur. Conf. on Comput. Vis. , 2024

2024
[131]

RePaint: Inpainting Using Denoising Diffusion Probabilistic Models,

A. Lugmayr, M. Danelljan, A. Romero, F. Yu, R. Timofte, and L. Van Gool, “RePaint: Inpainting Using Denoising Diffusion Probabilistic Models,” in Proc. IEEE/CVF Conf. on Comput. Vis. Pattern Recognit. , 2022

2022
[132]

Congested traffic states in empirical observations and microscopic simula- tions,

M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simula- tions,” Phys. Rev. E , 2000. PREPRINT 20

2000
[133]

DriveSceneGen: Generating Diverse and Realistic Driving Scenarios From Scratch,

S. Sun, Z. Gu, T. Sun, et al. , “DriveSceneGen: Generating Diverse and Realistic Driving Scenarios From Scratch,” IEEE Robotics Autom. Lett. , 2024

2024
[134]

Motion Trans- former with Global Intention Localization and Local Move- ment Refinement,

S. Shi, L. Jiang, D. Dai, and B. Schiele, “Motion Trans- former with Global Intention Localization and Local Move- ment Refinement,” Annu. Conf. on Neural Inf. Process. Syst., 2022

2022
[135]

PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models,

J. Chen, R. Deng, and Y . Furukawa, “PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models,” Adv. Neural Inf. Process. Syst. , 2023

2023
[136]

MapTRv2: An End-to- End Framework for Online Vectorized HD Map Construc- tion,

B. Liao, S. Chen, Y . Zhang, et al., “MapTRv2: An End-to- End Framework for Online Vectorized HD Map Construc- tion,” Int. J. Comput. Vis. , 2024

2024
[137]

TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenar- ios,

L. Feng, Q. Li, Z. Peng, S. Tan, and B. Zhou, “TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenar- ios,” 2023, arXiv:2210.06609

2023 arXiv
[138]

RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios,

W. Ding, Y . Cao, D. Zhao, C. Xiao, and M. Pavone, “RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios,” 2024, arXiv:2312.13303

2024 arXiv
[139]

Language Conditioned Traffic Generation,

S. Tan, B. Ivanovic, X. Weng, M. Pavone, and P. Krae- henbuehl, “Language Conditioned Traffic Generation,” 2023, arXiv:2307.07947

2023 arXiv
[140]

K. J. W. Craik, The Nature of Explanation . CUP Archive, 1967

1967
[141]

Cognitive maps in rats and men,

E. C. Tolman, “Cognitive maps in rats and men,” Psychol. Rev., 1948

1948
[142]

A. E. Bryson, Applied Optimal Control: Optimization, Esti- mation and Control . New York: Routledge, 2018

2018
[143]

Dyna, an integrated architecture for learning, planning, and reacting,

R. S. Sutton, “Dyna, an integrated architecture for learning, planning, and reacting,” SIGART Bull., 1991

1991
[144]

World Models,

D. Ha and J. Schmidhuber, “World Models,” 2018, arXiv:1803.10122

2018 arXiv
[145]

OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving,

W. Zheng, W. Chen, Y . Huang, B. Zhang, Y . Duan, and J. Lu, “OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving,” in Eur. Conf. on Comput. Vis. , 2023

2023
[146]

Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving,

Y . Wang, J. He, L. Fan, H. Li, Y . Chen, and Z. Zhang, “Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving,” 2023, arXiv:2311.17918

2023 arXiv
[147]

A Path Towards Autonomous Machine Intelli- gence,

Y . LeCun, “A Path Towards Autonomous Machine Intelli- gence,” Open Rev
[148]

Dream to Control: Learning Behaviors by Latent Imagination,

D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi, “Dream to Control: Learning Behaviors by Latent Imagination,” 2020, arXiv:1912.01603

2020 arXiv
[149]

Learning Latent Dynamics for Planning from Pixels,

D. Hafner, T. Lillicrap, I. Fischer, et al. , “Learning Latent Dynamics for Planning from Pixels,” in Proc. 36th Int. Conf. Mach. Learn., 2019

2019
[150]

Mas- tering Diverse Domains through World Models,

D. Hafner, J. Pasukonis, J. Ba, and T. Lillicrap, “Mas- tering Diverse Domains through World Models,” 2024, arXiv:2301.04104

2024 arXiv
[151]

Emerging Properties in Self-Supervised Vision Transformers,

M. Caron, H. Touvron, I. Misra, et al., “Emerging Properties in Self-Supervised Vision Transformers,” inProc. IEEE/CVF Int. Conf. on Comput. Vis. , 2021

2021
[152]

DriveDreamer: Towards Real-World-Drive World Models for Autonomous Driving,

X. Wang, Z. Zhu, G. Huang, X. Chen, J. Zhu, and J. Lu, “DriveDreamer: Towards Real-World-Drive World Models for Autonomous Driving,” in Comput. Vis. – Eur. Conf. on Comput. Vis. 2024 , 2025

2024
[153]

DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation,

G. Zhao, C. Ni, X. Wang, et al., “DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation,” 2024, arXiv:2410.13571

2024 arXiv
[154]

ADriver-I: A General World Model for Autonomous Driving,

F. Jia, W. Mao, Y . Liu, et al., “ADriver-I: A General World Model for Autonomous Driving,” 2023, arXiv:2311.13549

2023 arXiv
[155]

Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2),

Q. Li, X. Jia, S. Wang, and J. Yan, “Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2),” 2024, arXiv:2402.16720

2024 arXiv
[156]

CARLA: An Open Urban Driving Simulator,

A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V . Koltun, “CARLA: An Open Urban Driving Simulator,” 2017, arXiv:1711.03938

2017 arXiv
[157]

Enhancing End-to-End Autonomous Driving with Latent World Model,

Y . Li, L. Fan, J. He, et al. , “Enhancing End-to-End Autonomous Driving with Latent World Model,” 2024, arXiv:2406.08481

2024 arXiv
[158]

BEVWorld: A Mul- timodal World Model for Autonomous Driving via Unified BEV Latent Space,

Y . Zhang, S. Gong, K. Xiong, et al. , “BEVWorld: A Mul- timodal World Model for Autonomous Driving via Unified BEV Latent Space,” 2024, arXiv:2407.05679

2024 arXiv
[159]

MUVO: A Multimodal World Model with Spatial Representations for Autonomous Driving,

D. Bogdoll, Y . Yang, T. Joseph, and J. M. Zöllner, “MUVO: A Multimodal World Model with Spatial Representations for Autonomous Driving,” 2024, arXiv:2311.11762

2024 arXiv
[160]

Driving in the Occupancy World: Vision-Centric 4D Occupancy Forecasting and Plan- ning via World Models for Autonomous Driving,

Y . Yang, J. Mei, Y . Ma, et al. , “Driving in the Occupancy World: Vision-Centric 4D Occupancy Forecasting and Plan- ning via World Models for Autonomous Driving,” 2025, arXiv:2408.14197

2025 arXiv
[161]

nuScenes: A Multimodal Dataset for Autonomous Driving,

H. Caesar, V . Bankiti, A. H. Lang, et al. , “nuScenes: A Multimodal Dataset for Autonomous Driving,” in 2020 IEEE/CVF Conf. on Comput. Vis. Pattern Recognit. , 2020

2020
[162]

OccLLaMA: An Occupancy-Language-Action Gen- erative World Model for Autonomous Driving,

J. Wei, S. Yuan, P. Li, Q. Hu, Z. Gan, and W. Ding, “OccLLaMA: An Occupancy-Language-Action Gen- erative World Model for Autonomous Driving,” 2024, arXiv:2409.03272

2024 arXiv
[163]

Wayformer: Motion forecasting via simple & efficient attention networks,

N. Nayakanti, R. Al-Rfou, A. Zhou, K. Goel, K. S. Refaat, and B. Sapp, “Wayformer: Motion forecasting via simple & efficient attention networks,” in IEEE Int. Conf. on Robotics & Autom., 2023

2023
[164]

Actionformer: Localizing moments of actions with transformers,

C.-L. Zhang, J. Wu, and Y . Li, “Actionformer: Localizing moments of actions with transformers,” in Eur. Conf. on Comput. Vis., 2022

2022
[165]

RedMotion: Motion Prediction via Redundancy Reduction,

R. Wagner, O. S. Tas, M. Klemp, C. F. Lopez, and C. Stiller, “RedMotion: Motion Prediction via Redundancy Reduction,” Trans. on Mach. Learn. Res. , 2024

2024
[166]

Joint- Motion: Joint Self-supervision for Joint Motion Prediction,

R. Wagner, O. S. Tas, M. Klemp, and C. Fernandez, “Joint- Motion: Joint Self-supervision for Joint Motion Prediction,” in Conf. on Robot Learn. , 2024

2024
[167]

Language-Driven Interactive Traffic Trajectory Generation,

J. Xia, C. Xu, Q. Xu, C. Xie, Y . Wang, and S. Chen, “Language-Driven Interactive Traffic Trajectory Generation,” Annu. Conf. on Neural Inf. Process. Syst. , 2024

2024
[168]

Efficient Interaction- Aware Trajectory Prediction Model Based on Multi-head Attention,

Z. Peng, J. Yan, H. Yin, et al. , “Efficient Interaction- Aware Trajectory Prediction Model Based on Multi-head Attention,” Automot. Innov., 2024

2024
[169]

Social LSTM: Human Trajectory Prediction in Crowded Spaces,

A. Alahi, K. Goel, V . Ramanathan, A. Robicquet, L. Fei- Fei, and S. Savarese, “Social LSTM: Human Trajectory Prediction in Crowded Spaces,” in IEEE Conf. Comput. Vis. Pattern Recognit., 2016

2016
[170]

Multiple Futures Predic- tion,

Y . C. Tang and R. Salakhutdinov, “Multiple Futures Predic- tion,” in 32nd Annu. Conf. on Neural Inf. Process. Syst. , 2019

2019
[171]

WaveNet: A Generative Model for Raw Audio,

A. v. d. Oord, S. Dieleman, H. Zen, et al. , “WaveNet: A Generative Model for Raw Audio,” 2016, arXiv:1609.03499

2016 arXiv
[172]

LatentFormer: Multi-Agent Transformer-Based In- teraction Modeling and Trajectory Prediction,

E. A. Abolfathi, A. Rasouli, P. Lakner, M. Rohani, and J. Luo, “LatentFormer: Multi-Agent Transformer-Based In- teraction Modeling and Trajectory Prediction,” 2022, arXiv: 2203.01880

2022 arXiv
[173]

Large Trajectory Models are Scalable Motion Predictors and Planners,

Q. Sun, S. Zhang, D. Ma, et al., “Large Trajectory Models are Scalable Motion Predictors and Planners,” 2023, arXiv: 2310.19620

2023 arXiv
[174]

AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving,

X. Jia, S. Shi, Z. Chen, et al. , “AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving,” 2024, arXiv: 2403.13331

2024 arXiv
[175]

MotionTransformer: Transferring Neural Inertial Tracking between Domains,

C. Chen, Y . Miao, C. X. Lu, et al. , “MotionTransformer: Transferring Neural Inertial Tracking between Domains,” in 33rd AAAI Conf. Artif. Intell. , 2019

2019
[176]

Trajeglish: Learning the Language of Driving Scenarios,

J. Philion, X. B. Peng, and S. Fidler, “Trajeglish: Learning the Language of Driving Scenarios,” 2023, arXiv:2312.04535. PREPRINT 21

2023 arXiv
[177]

Words in Motion: Extracting Interpretable Control Vectors for Motion Transformers,

O. S. Tas and R. Wagner, “Words in Motion: Extracting Interpretable Control Vectors for Motion Transformers,” in Int. Conf. on Learn. Represent. , 2025

2025
[178]

Revisit Mixture Models for Multi-Agent Simulation: Experimental Study within a Unified Framework,

L. Lin, X. Lin, K. Xu, et al. , “Revisit Mixture Models for Multi-Agent Simulation: Experimental Study within a Unified Framework,” 2025, arXiv:2501.17015

2025 arXiv
[179]

SMART: Scalable Multi-agent Real-time Motion Generation via Next-token Prediction,

W. Wu, X. Feng, Z. Gao, and Y . Kan, “SMART: Scalable Multi-agent Real-time Motion Generation via Next-token Prediction,” in Adv. Neural Inf. Process. Syst. , 2024

2024
[180]

DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction,

Y . Choi, R. C. Mercurius, S. M. A. Shabestary, and A. Rasouli, “DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction,” 2023, arXiv:2310.14570

2023 arXiv
[182]

Generating driving scenes with diffusion,

E. Pronovost, K. Wang, and N. Roy, “Generating driving scenes with diffusion,” 2023, arXiv:2305.18452

2023 arXiv
[183]

Plan- ning with diffusion for flexible behavior synthesis,

M. Janner, Y . Du, J. B. Tenenbaum, and S. Levine, “Plan- ning with diffusion for flexible behavior synthesis,” 2022, arXiv:2205.09991

2022 arXiv
[184]

Monitoring temporal properties of continuous signals,

O. Maler and D. Nickovic, “Monitoring temporal properties of continuous signals,” in Int. Symp. on Formal Tech. Real- Time Fault-Tolerant Syst., 2004

2004
[185]

Madiff: Offline multi-agent learning with diffusion models,

Z. Zhu, M. Liu, L. Mao, et al., “Madiff: Offline multi-agent learning with diffusion models,” Adv. Neural Inf. Process. Syst., 2025

2025
[186]

Diffusion Policies as Multi-Agent Reinforcement Learning Strategies,

J. Geng, X. Liang, H. Wang, and Y . Zhao, “Diffusion Policies as Multi-Agent Reinforcement Learning Strategies,” in Int. Conf. on Artif. Neural Networks , 2023

2023
[187]

Improving and generalizing flow-based generative models with minibatch optimal transport,

A. Tong, K. Fatras, N. Malkin, et al. , “Improving and generalizing flow-based generative models with minibatch optimal transport,” 2023, arXiv:2302.00482

2023 arXiv
[188]

Efficient trajectory forecasting and generation with conditional flow matching,

S. Ye and M. C. Gombolay, “Efficient trajectory forecasting and generation with conditional flow matching,” in 2024 IEEE/RSJ Int. Conf. on Intell. Robots Syst. , 2024

2024
[189]

Trajectory prediction with latent belief energy-based model,

B. Pang, T. Zhao, X. Xie, and Y . N. Wu, “Trajectory prediction with latent belief energy-based model,” in IEEE / CVF Comput. Vis. Pattern Recognit. Conf. - Workshop, 2021

2021
[190]

SEEM: A sequence entropy energy-based model for pedestrian trajectory all-then-one prediction,

D. Wang, H. Liu, N. Wang, Y . Wang, H. Wang, and S. McLoone, “SEEM: A sequence entropy energy-based model for pedestrian trajectory all-then-one prediction,” IEEE Trans. on Pattern Anal. Mach. Intell. , 2022

2022
[191]

Modeling Pedes- trian Intrinsic Uncertainty for Multimodal Stochastic Tra- jectory Prediction via Energy Plan Denoising,

Y . Liu, Q. Z. Sheng, and L. Yao, “Modeling Pedes- trian Intrinsic Uncertainty for Multimodal Stochastic Tra- jectory Prediction via Energy Plan Denoising,” 2024, arXiv:2405.07164

2024 arXiv
[192]

Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments,

B. Brito, B. Floor, L. Ferranti, and J. Alonso-Mora, “Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments,” IEEE Robotics Au- tom. Lett., 2019

2019
[193]

NMPC trajectory planner for urban autonomous driving,

F. Micheli, M. Bersani, S. Arrigoni, F. Braghin, and F. Cheli, “NMPC trajectory planner for urban autonomous driving,” Veh. Syst. Dyn., 2023

2023
[194]

Decision-theoretic MPC: Motion Planning with Weighted Maneuver Prefer- ences Under Uncertainty,

Ö. ¸ S. Ta¸ s, P. H. Brusius, and C. Stiller, “Decision-theoretic MPC: Motion Planning with Weighted Maneuver Prefer- ences Under Uncertainty,” 2024, arXiv:2310.17963

2024 arXiv
[195]

DESPOT: Online POMDP Planning with Regularization,

A. Somani, N. Ye, D. Hsu, and W. S. Lee, “DESPOT: Online POMDP Planning with Regularization,” in Annu. Conf. on Neural Inf. Process. Syst. , 2013

2013
[196]

Flexible unit A-star trajectory planning for au- tonomous vehicles on structured road maps,

Z. Boroujeni, D. Goehring, F. Ulbrich, D. Neumann, and R. Rojas, “Flexible unit A-star trajectory planning for au- tonomous vehicles on structured road maps,” in IEEE Int. Conf. Veh. Electron. Saf. , 2017

2017
[197]

MOD-RRT*: A Sampling- Based Algorithm for Robot Path Planning in Dynamic Environment,

J. Qi, H. Yang, and H. Sun, “MOD-RRT*: A Sampling- Based Algorithm for Robot Path Planning in Dynamic Environment,” IEEE Trans. on Ind. Electron. , 2021

2021
[198]

Learning-Aided Warmstart of Model Predictive Control in Uncertain Fast-Changing Traffic,

M.-K. Bouzidi, Y . Yao, D. Goehring, and J. Reichardt, “Learning-Aided Warmstart of Model Predictive Control in Uncertain Fast-Changing Traffic,” in 2024 IEEE Int. Conf. on Robotics Autom. , 2024

2024
[199]

Interactive Multi-Modal Motion Planning With Branch Model Predictive Control,

Y . Chen, U. Rosolia, W. Ubellacker, N. Csomay-Shanklin, and A. D. Ames, “Interactive Multi-Modal Motion Planning With Branch Model Predictive Control,” IEEE Robotics Autom. Lett., 2022

2022
[200]

Efficient Sampling in POMDPs with Lipschitz Bandits for Motion Planning in Continuous Spaces,

Ö. ¸ S. Ta¸ s, F. Hauser, and M. Lauer, “Efficient Sampling in POMDPs with Lipschitz Bandits for Motion Planning in Continuous Spaces,” in Proc. IEEE Intell. Veh. Symp., 2021

2021
[201]

Unfreezing the robot: Navi- gation in dense, interacting crowds,

P. Trautman and A. Krause, “Unfreezing the robot: Navi- gation in dense, interacting crowds,” in 2010 IEEE/RSJ Int. Conf. on Intell. Robots Syst. , 2010

2010
[202]

Interaction-Aware Merging in Mixed Traffic with Integrated Game-theoretic Predictive Control and Inverse Differential Game,

M.-K. Bouzidi and E. Hashemi, “Interaction-Aware Merging in Mixed Traffic with Integrated Game-theoretic Predictive Control and Inverse Differential Game,” in 2023 IEEE Intell. Veh. Symp., 2023

2023
[203]

Memory-based crowd-aware robot navigation using deep reinforcement learning,

S. S. Samsani, H. Mutahira, and M. S. Muhammad, “Memory-based crowd-aware robot navigation using deep reinforcement learning,” Complex & Intell. Syst. , 2023

2023
[204]

Deep-PANTHER: Learning- Based Perception-Aware Trajectory Planner in Dynamic En- vironments,

J. Tordesillas and J. P. How, “Deep-PANTHER: Learning- Based Perception-Aware Trajectory Planner in Dynamic En- vironments,” IEEE Robotics Autom. Lett. , 2023

2023
[205]

Merging in Congested Freeway Traffic Using Multipolicy Decision Making and Passive Actor-Critic Learning,

T. Nishi, P. Doshi, and D. Prokhorov, “Merging in Congested Freeway Traffic Using Multipolicy Decision Making and Passive Actor-Critic Learning,” IEEE Intell. Veh. Symp. , 2019

2019
[206]

Drive Like a Human: Rethink- ing Autonomous Driving with Large Language Models,

D. Fu, X. Li, L. Wen, et al., “Drive Like a Human: Rethink- ing Autonomous Driving with Large Language Models,” in IEEE/CVF Winter Conf. on Appl. Comput. Vis. 2024 , 2023

2024
[207]

GPT-Driver: Learning to Drive with GPT,

J. Mao, Y . Qian, J. Ye, H. Zhao, and Y . Wang, “GPT-Driver: Learning to Drive with GPT,” in Annu. Conf. on Neural Inf. Process. Syst. Workshop, 2023

2023
[208]

Can Vehicle Motion Planning Generalize to Realistic Long- tail Scenarios?

M. Hallgarten, J. Zapata, M. Stoll, K. Renz, and A. Zell, “Can Vehicle Motion Planning Generalize to Realistic Long- tail Scenarios?” 2024, arXiv:2404.07569

2024 arXiv
[209]

Making Large Language Models Better Planners with Reasoning-Decision Align- ment,

Z. Huang, T. Tang, S. Chen, et al., “Making Large Language Models Better Planners with Reasoning-Decision Align- ment,” in Eur. Conf. on Comput. Vis. 2024 , 2024

2024
[210]

Data-driven Economic NMPC us- ing Reinforcement Learning,

S. Gros and M. Zanon, “Data-driven Economic NMPC us- ing Reinforcement Learning,” IEEE Trans. Autom. Control., 2020

2020
[211]

Weights-varying MPC for Autonomous Vehicle Guidance: A Deep Reinforcement Learning Ap- proach,

B. Zarrouki, V . Klös, N. Heppner, S. Schwan, R. Ritschel, and R. V oßwinkel, “Weights-varying MPC for Autonomous Vehicle Guidance: A Deep Reinforcement Learning Ap- proach,” in 2021 Eur. Control. Conf. , 2021

2021
[212]

Where to go Next: Learning a Subgoal Recommendation Policy for Navigation in Dynamic Environments,

B. Brito, M. Everett, J. P. How, and J. Alonso-Mora, “Where to go Next: Learning a Subgoal Recommendation Policy for Navigation in Dynamic Environments,” IEEE Robotics Autom. Lett., 2021

2021
[213]

Learning from the Hindsight Plan – Episodic MPC Im- provement,

A. Tamar, G. Thomas, T. Zhang, S. Levine, and P. Abbeel, “Learning from the Hindsight Plan – Episodic MPC Im- provement,” 2017, arXiv: 1609.09001

2017 arXiv
[214]

Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning,

T. Koller, F. Berkenkamp, M. Turchetta, J. Boedecker, and A. Krause, “Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning,” 2019, arXiv: 1906.12189

2019 arXiv
[215]

Data-Driven Model Predictive Control with Stability and Robustness Guarantees,

J. Berberich, J. Köhler, M. A. Müller, and F. Allgöwer, “Data-Driven Model Predictive Control with Stability and Robustness Guarantees,” IEEE Trans. on Autom. Control. , 2021

2021
[216]

Learning-Based Model Predictive Control: To- ward Safe Learning in Control,

L. Hewing, K. P. Wabersich, M. Menner, and M. N. Zeilinger, “Learning-Based Model Predictive Control: To- ward Safe Learning in Control,” Annu. Rev. Control. Robotics, Auton. Syst. , 2020

2020
[217]

Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios,

B. Brito, A. Agarwal, and J. Alonso-Mora, “Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios,” IEEE Trans. on Intell. Transp. Syst., 2022. PREPRINT 22

2022
[218]

Policy Search for Model Predictive Control with Application to Agile Drone Flight,

Y . Song and D. Scaramuzza, “Policy Search for Model Predictive Control with Application to Agile Drone Flight,” 2021, arXiv: 2112.03850

2021 arXiv
[219]

Predictive Con- trol Barrier Functions: Enhanced Safety Mechanisms for Learning-Based Control,

K. P. Wabersich and M. N. Zeilinger, “Predictive Con- trol Barrier Functions: Enhanced Safety Mechanisms for Learning-Based Control,” IEEE Trans. on Autom. Control. , 2023

2023
[220]

A Predictive Safety Filter for Learning-Based Racing Con- trol,

B. Tearle, K. P. Wabersich, A. Carron, and M. N. Zeilinger, “A Predictive Safety Filter for Learning-Based Racing Con- trol,” IEEE Robotics Autom. Lett. , 2021

2021
[221]

Learning initial trajectory using sequence- to-sequence approach to warm start an optimization-based motion planner,

S. Natarajan, “Learning initial trajectory using sequence- to-sequence approach to warm start an optimization-based motion planner,” in IEEE/RSJ Int. Conf. on Intell. Robots Syst., 2021

2021
[222]

Learning Model Predictive Controllers with Real-Time Attention for Real- World Navigation,

X. Xiao, T. Zhang, K. Choromanski, et al., “Learning Model Predictive Controllers with Real-Time Attention for Real- World Navigation,” 2022, arXiv: 2209.10780

2022 arXiv
[223]

GAN-MPC: Training Model Predictive Con- trollers with Parameterized Cost Functions using Demonstra- tions from Non-identical Experts,

R. Burnwal, A. Santara, N. P. Bhatt, B. Ravindran, and G. Aggarwal, “GAN-MPC: Training Model Predictive Con- trollers with Parameterized Cost Functions using Demonstra- tions from Non-identical Experts,” 2023, arXiv: 2305.19111

2023 arXiv
[224]

LanguageMPC: Large Lan- guage Models as Decision Makers for Autonomous Driving,

H. Sha, Y . Mu, Y . Jiang, et al., “LanguageMPC: Large Lan- guage Models as Decision Makers for Autonomous Driving,” 2023, arXiv:2310.03026

2023 arXiv
[225]

Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model,

F. Lotfi, K. Virji, F. Faraji, et al., “Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model,” in 2024 IEEE Int. Conf. on Robotics Autom. , 2024

2024
[226]

One Model to Drift Them All: Physics-Informed Conditional Diffusion Model for Driving at the Limits,

F. Djeumou, T. J. Lew, N. Ding, et al., “One Model to Drift Them All: Physics-Informed Conditional Diffusion Model for Driving at the Limits,” in Proc. The 8th Conf. on Robot Learn., 2025

2025
[227]

Diffusion Model Predictive Control,

G. Zhou, S. Swaminathan, R. V . Raju, et al. , “Diffusion Model Predictive Control,” 2024, arXiv:2410.05364

2024 arXiv
[228]

Conditional Generative Adversarial Networks for Optimal Path Plan- ning,

N. Ma, J. Wang, J. Liu, and M. Q.-H. Meng, “Conditional Generative Adversarial Networks for Optimal Path Plan- ning,” IEEE Trans. on Cogn. Dev. Syst. , 2022

2022
[229]

Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows,

G. Rabenstein, L. Ullrich, and K. Graichen, “Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows,” in 2024 IEEE Intell. Veh. Symp. (IV), 2024

2024
[230]

Empowering Au- tonomous Driving with Large Language Models: A Safety Perspective,

Y . Wang, R. Jiao, S. S. Zhan, et al. , “Empowering Au- tonomous Driving with Large Language Models: A Safety Perspective,” in Int. Conf. on Learn. Represent. 2024 Work- shop LLMAgent, 2024

2024
[231]

CoBL-Diffusion: Diffusion- Based Conditional Robot Planning in Dynamic Environ- ments Using Control Barrier and Lyapunov Functions,

K. Mizuta and K. Leung, “CoBL-Diffusion: Diffusion- Based Conditional Robot Planning in Dynamic Environ- ments Using Control Barrier and Lyapunov Functions,” 2024, arXiv:2406.05309

2024 arXiv
[232]

SafeDiffuser: Safe Planning with Diffusion Probabilistic Models,

W. Xiao, T.-H. Wang, C. Gan, and D. Rus, “SafeDiffuser: Safe Planning with Diffusion Probabilistic Models,” 2023, arXiv:2306.00148

2023 arXiv
[233]

DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Mod- els,

X. Tian, J. Gu, B. Li, et al., “DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Mod- els,” 2024, arXiv:2402.12289

2024 arXiv
[234]

LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning,

S. Meng, Y . Wang, C.-F. Yang, N. Peng, and K.-W. Chang, “LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning,” in Conf. on Empir. Methods Nat. Lang. Process. , 2024

2024
[235]

Towards fully autonomous driving: Systems and algorithms,

J. Levinson, J. Askeland, J. Becker, et al. , “Towards fully autonomous driving: Systems and algorithms,” in 2011 IEEE Intell. Veh. Symp., 2011

2011
[236]

A functional reference archi- tecture for autonomous driving,

S. Behere and M. Törngren, “A functional reference archi- tecture for autonomous driving,” Inf. Softw. Technol., 2016

2016
[237]

Autonomous Vehicle: The Architecture Aspect of Self Driving Car,

F. Munir, S. Azam, M. I. Hussain, A. M. Sheri, and M. Jeon, “Autonomous Vehicle: The Architecture Aspect of Self Driving Car,” in Int. Conf. on Sensors, Signal Image Process., 2018

2018
[238]

Auto- mated vehicle system architecture with performance assess- ment,

O. S. Tas, S. Hormann, B. Schaufele, and F. Kuhnt, “Auto- mated vehicle system architecture with performance assess- ment,” in 2017 IEEE 20th Int. Conf. on Intell. Transp. Syst. , 2017

2017
[239]

A Survey of End-to-End Driving: Archi- tectures and Training Methods,

A. Tampuu, T. Matiisen, M. Semikin, D. Fishman, and N. Muhammad, “A Survey of End-to-End Driving: Archi- tectures and Training Methods,” IEEE Trans. on Neural Networks Learn. Syst. , 2022

2022
[240]

Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driv- ing,

B. Jiang, S. Chen, B. Liao, et al. , “Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driv- ing,” 2024, arXiv:2410.22313

2024 arXiv
[241]

Recent Advancements in End-to- End Autonomous Driving using Deep Learning: A Survey,

P. S. Chib and P. Singh, “Recent Advancements in End-to- End Autonomous Driving using Deep Learning: A Survey,” 2023, arXiv:2307.04370

2023 arXiv
[242]

End-to-End Autonomous Driving: Challenges and Frontiers,

L. Chen, P. Wu, K. Chitta, B. Jaeger, A. Geiger, and H. Li, “End-to-End Autonomous Driving: Challenges and Frontiers,” IEEE Trans. on Pattern Anal. Mach. Intell., 2024

2024
[243]

Planning-oriented Au- tonomous Driving,

Y . Hu, J. Yang, L. Chen, et al. , “Planning-oriented Au- tonomous Driving,” 2023, arXiv:2212.10156

2023 arXiv
[244]

SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation,

W. Sun, X. Lin, Y . Shi, C. Zhang, H. Wu, and S. Zheng, “SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation,” 2024, arXiv:2405.19620

2024 arXiv
[245]

Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following,

B. Yang, H. Su, N. Gkanatsios, et al. , “Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following,” 2024, arXiv:2402.06559

2024 arXiv
[246]

DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving,

B. Liao, S. Chen, H. Yin, et al., “DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving,” 2024, arXiv:2411.15139

2024 arXiv
[247]

PARA-Drive: Parallelized Architecture for Real-Time Au- tonomous Driving,

X. Weng, B. Ivanovic, Y . Wang, Y . Wang, and M. Pavone, “PARA-Drive: Parallelized Architecture for Real-Time Au- tonomous Driving,” in 2024 IEEE/CVF Conf. on Comput. Vis. Pattern Recognit., 2024

2024
[248]

V ADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning,

S. Chen, B. Jiang, H. Gao, et al. , “V ADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning,” 2024, arXiv:2402.13243

2024 arXiv
[249]

Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation,

Z. Li, K. Li, S. Wang, et al. , “Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation,” 2024, arXiv:2406.06978

2024 arXiv
[250]

DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba,

C. Yuan, Z. Zhang, J. Sun, et al. , “DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba,” 2024, arXiv:2408.03601

2024 arXiv
[251]

DriveGPT4: Inter- pretable End-to-end Autonomous Driving via Large Lan- guage Model,

Z. Xu, Y . Zhang, E. Xie, et al. , “DriveGPT4: Inter- pretable End-to-end Autonomous Driving via Large Lan- guage Model,” in IEEE Robotics Autom. Lett. , 2024

2024
[252]

OmniDrive: A Holistic LLM-Agent Framework for Autonomous Driv- ing with 3D Perception, Reasoning and Planning,

S. Wang, Z. Yu, X. Jiang, et al. , “OmniDrive: A Holistic LLM-Agent Framework for Autonomous Driv- ing with 3D Perception, Reasoning and Planning,” 2024, arXiv:2405.01533

2024 arXiv
[253]

CarLLaV A: Vision language models for camera-only closed-loop driving,

K. Renz, L. Chen, A.-M. Marcu, et al., “CarLLaV A: Vision language models for camera-only closed-loop driving,” 2024, arXiv:2406.10165

2024 arXiv
[254]

EMMA: End-to- End Multimodal Model for Autonomous Driving,

J.-J. Hwang, R. Xu, H. Lin, et al. , “EMMA: End-to- End Multimodal Model for Autonomous Driving,” 2024, arXiv:2410.23262

2024 arXiv
[255]

Gemini: A Family of Highly Capable Multimodal Models,

G. Team, R. Anil, S. Borgeaud, et al. , “Gemini: A Family of Highly Capable Multimodal Models,” 2024, arXiv:2312.11805

2024 arXiv
[256]

BEVDriver: Leverag- ing BEV Maps in LLMs for Robust Closed-Loop Driving,

K. Winter, M. Azer, and F. B. Flohr, “BEVDriver: Leverag- ing BEV Maps in LLMs for Robust Closed-Loop Driving,” 2025, arXiv:2503.03074 [cs]

2025 arXiv
[257]

IMPALA: Scal- able Distributed Deep-RL with Importance Weighted Actor- Learner Architectures,

L. Espeholt, H. Soyer, R. Munos, et al. , “IMPALA: Scal- able Distributed Deep-RL with Importance Weighted Actor- Learner Architectures,” 2018, arXiv:1802.01561

2018 arXiv
[258]

Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generation,

E. Ma, L. Zhou, T. Tang, et al., “Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generation,” 2024, arXiv:2406.01349. PREPRINT 23

2024 arXiv
[261]

DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving,

X. Yang, L. Wen, Y . Ma, et al., “DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving,” 2024, arXiv:2408.00415

2024 arXiv
[262]

HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving,

H. Zhou, L. Lin, J. Wang, et al., “HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving,” 2024, arXiv:2412.01718

2024 arXiv
[263]

Argoverse 2: Next Generation Datasets for Self-Driving Perception and Fore- casting,

B. Wilson, W. Qi, T. Agarwal, et al. , “Argoverse 2: Next Generation Datasets for Self-Driving Perception and Fore- casting,” 2023, arXiv:2301.00493

2023 arXiv
[264]

Argov- erse: 3D Tracking and Forecasting with Rich Maps,

M.-F. Chang, J. Lambert, P. Sangkloy, et al. , “Argov- erse: 3D Tracking and Forecasting with Rich Maps,” 2019, arXiv:1911.02620

2019 arXiv
[265]

INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps,

W. Zhan, L. Sun, D. Wang, et al., “INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps,” 2019, arXiv:1910.03088

2019 arXiv
[266]

One Thousand and One Hours: Self-driving Motion Prediction Dataset,

J. Houston, G. Zuidhof, L. Bergamini, et al., “One Thousand and One Hours: Self-driving Motion Prediction Dataset,” 2020, arXiv:2006.14480

2020 arXiv
[267]

Large Scale Inter- active Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset,

S. Ettinger, S. Cheng, B. Caine, et al. , “Large Scale Inter- active Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset,” 2021, arXiv:2104.10133

2021 arXiv
[268]

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks,

A. Malinin, N. Band, Ganshin, et al. , “Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks,” 2022, arXiv:2107.07455

2022 arXiv
[269]

Unreal Engine,

“Unreal Engine,” 2014, https://www.unrealengine.com/de, Citation Key: games_epic_unreal_2014

2014
[270]

Bassermann, “Asam,” 2025, https://www.asam.net/

D. Bassermann, “Asam,” 2025, https://www.asam.net/

2025
[271]

Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation,

J. Hossain, “Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation,” 2023, arXiv:2306.11217

2023 arXiv
[272]

P4P: Conflict-Aware Motion Prediction for Planning in Au- tonomous Driving,

Q. Sun, X. Huang, B. C. Williams, and H. Zhao, “P4P: Conflict-Aware Motion Prediction for Planning in Au- tonomous Driving,” 2022, arXiv:2211.01634

2022 arXiv
[273]

Trajectory-guided Control Prediction for End-to-end Au- tonomous Driving: A Simple yet Strong Baseline,

P. Wu, X. Jia, L. Chen, J. Yan, H. Li, and Y . Qiao, “Trajectory-guided Control Prediction for End-to-end Au- tonomous Driving: A Simple yet Strong Baseline,” 2022, arXiv:2206.08129

2022 arXiv
[274]

Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections,

J. Roh, C. Mavrogiannis, R. Madan, D. Fox, and S. S. Srinivasa, “Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections,” 2020, arXiv:2011.03894

2020 arXiv
[275]

Micro- scopic Traffic Simulation using SUMO,

P. A. Lopez, M. Behrisch, L. Bieker-Walz, et al. , “Micro- scopic Traffic Simulation using SUMO,” in 2018 21st Int. Conf. on Intell. Transp. Syst. , 2018

2018
[276]

Interfacing a Traffic Light Controller with SUMO for Hardware-in-the-Loop Testing,

R. Markowski and J. Trumpold, “Interfacing a Traffic Light Controller with SUMO for Hardware-in-the-Loop Testing,” in SUMO User Conf. , 2024

2024
[277]

Vehicle to Everything (V2X) Communication Protocol by Using Vehicular AD-HOC Network,

M. A. Naeem, X. Jia, M. A. Saleem, et al. , “Vehicle to Everything (V2X) Communication Protocol by Using Vehicular AD-HOC Network,” in 2020 17th Int. Comput. Conf. on Wavelet Active Media Technol. Inf. Process. , 2020

2020
[278]

Planet OSM,

“Planet OSM,” 2012, https://planet.openstreetmap.org/

2012
[279]

Deep reinforcement learning for traffic light control optimization in multi-modal simulation of SUMO,

Y . Xu, “Deep reinforcement learning for traffic light control optimization in multi-modal simulation of SUMO,” Ph.D. dissertation, TU Delft, Delft, Nederlands, 2024

2024
[280]

Deep reinforcement-learning-based driving policy for autonomous road vehicles,

K. Makantasis, M. Kontorinaki, and I. Nikolos, “Deep reinforcement-learning-based driving policy for autonomous road vehicles,” IET Intell. Transp. Syst. , 2020

2020
[281]

Coupling SUMO with a Motion Planning Framework for Automated Vehicles,

M. Klischat, O. Dragoi, M. Eissa, and M. Althoff, “Coupling SUMO with a Motion Planning Framework for Automated Vehicles,” in SUMO User Conf. 2019

2019
[282]

Motion Prediction and Manoeuvre Planning,

A. Artuñedo, “Motion Prediction and Manoeuvre Planning,” in Decis. Strateg. for Autom. Driv. Urban Environ. 2020

2020
[283]

Trajectory Planning for Automated Merging Vehicles on Freeway Acceleration Lane,

M. Gu, Y . Su, C. Wang, and Y . Guo, “Trajectory Planning for Automated Merging Vehicles on Freeway Acceleration Lane,” IEEE Trans. Veh. Technol., 2024

2024
[284]

TORCS, The Open Racing Car Simulator,

E. Espié, C. Guionneau, B. Wymann, C. Dimitrakakis, R. Coulom, and A. Sumner, “TORCS, The Open Racing Car Simulator,” in Semantic Scholar, 2005

2005
[285]

Learning to overtake in TORCS using simple reinforcement learning,

D. Loiacono, A. Prete, P. L. Lanzi, and L. Cardamone, “Learning to overtake in TORCS using simple reinforcement learning,” in IEEE Congr. on Evol. Comput. , 2010

2010
[286]

Deep Reinforcement Learn- ing for Autonomous Driving,

S. Wang, D. Jia, and X. Weng, “Deep Reinforcement Learn- ing for Autonomous Driving,” 2019, arXiv:1811.11329

2019 arXiv
[287]

Pas 21448-road vehicles-safety of the intended func- tionality,

I. Iso, “Pas 21448-road vehicles-safety of the intended func- tionality,” Int. Organ. for Stand. , 2019

2019
[288]

Improving Transferability of Adversarial Examples With Input Diversity,

C. Xie, Z. Zhang, Y . Zhou, et al., “Improving Transferability of Adversarial Examples With Input Diversity,” in 2019 IEEE/CVF Conf. on Comput. Vis. Pattern Recognit. , 2019

2019
[289]

Unlabeled Data Improves Adversarial Robustness,

Y . Carmon, A. Raghunathan, L. Schmidt, J. C. Duchi, and P. S. Liang, “Unlabeled Data Improves Adversarial Robustness,” in Adv. Neural Inf. Process. Syst. , 2019

2019
[290]

Knowledge Augmented Machine Learning with Applications in Au- tonomous Driving: A Survey,

J. Wörmann, D. Bogdoll, C. Brunner, et al. , “Knowledge Augmented Machine Learning with Applications in Au- tonomous Driving: A Survey,” 2023, arXiv:2205.04712

2023 arXiv
[291]

Plausibility Verification For 3D Object Detectors Using Energy-Based Optimization,

A. Vivekanandan, N. Maier, and J. M. Zoellner, “Plausibility Verification For 3D Object Detectors Using Energy-Based Optimization,” 2022, arXiv:2211.05233

2022 arXiv
[292]

Hamilton–Jacobi Reachabil- ity: Some Recent Theoretical Advances and Applications in Unmanned Airspace Management,

M. Chen and C. J. Tomlin, “Hamilton–Jacobi Reachabil- ity: Some Recent Theoretical Advances and Applications in Unmanned Airspace Management,” Annu. Rev. Control. Robotics, Auton. Syst. , 2018

2018
[293]

Data-Driven Safety Filters: Hamilton-Jacobi Reachability, Control Barrier Functions, and Predictive Methods for Uncertain Systems,

K. P. Wabersich, A. J. Taylor, J. J. Choi, et al., “Data-Driven Safety Filters: Hamilton-Jacobi Reachability, Control Barrier Functions, and Predictive Methods for Uncertain Systems,” IEEE Control. Syst. Mag. , 2023

2023
[294]

The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems,

K.-C. Hsu, H. Hu, and J. F. Fisac, “The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems,” 2023, arXiv:2309.05837

2023 arXiv
[295]

Predictive control barrier functions: Enhanced safety mechanisms for learning- based control,

K. P. Wabersich and M. N. Zeilinger, “Predictive control barrier functions: Enhanced safety mechanisms for learning- based control,” 2022, arXiv:2105.10241

2022 arXiv
[296]

Learning Maximal Safe Sets Using Hypernetworks for MPC-based Local Trajectory Planning in Unknown Envi- ronments,

B. Deraji ´c, M.-K. Bouzidi, S. Bernhard, and W. Hönig, “Learning Maximal Safe Sets Using Hypernetworks for MPC-based Local Trajectory Planning in Unknown Envi- ronments,” 2025, arXiv:2410.20267

2025 arXiv
[297]

Mechanistic Interpretability for AI Safety – A Review,

L. Bereska and E. Gavves, “Mechanistic Interpretability for AI Safety – A Review,” 2024, arXiv:2404.14082

2024 arXiv
[298]

Ex- plainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions,

S. Atakishiyev, M. Salameh, H. Yao, and R. Goebel, “Ex- plainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions,” 2024, arXiv:2112.11561

2024 arXiv
[299]

On-Device Language Models: A Comprehensive Review,

J. Xu, Z. Li, W. Chen, et al., “On-Device Language Models: A Comprehensive Review,” 2024, arXiv: 2409.00088

2024 arXiv
[300]

MiniLLM: Knowl- edge Distillation of Large Language Models,

Y . Gu, L. Dong, F. Wei, and M. Huang, “MiniLLM: Knowl- edge Distillation of Large Language Models,” in The Twelfth Int. Conf. on Learn. Represent. , 2024

2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [259]

Parting with Misconceptions about Learning-based Vehicle Motion Planning,

D. Dauner, M. Hallgarten, A. Geiger, and K. Chitta, “Parting with Misconceptions about Learning-based Vehicle Motion Planning,” 2023, arXiv:2306.07962

arXiv 2023

[2] [260]

NA VSIM: Data- Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking,

D. Dauner, M. Hallgarten, T. Li, et al. , “NA VSIM: Data- Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking,” 2024, arXiv:2406.15349

arXiv 2024

[3] [181]

High-Resolution Image Synthesis with Latent Dif- fusion Models,

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis with Latent Dif- fusion Models,” 2022, arXiv:2112.10752

arXiv 2022

[4] [1]

The cityscapes dataset for semantic urban scene understanding,

M. Cordts, M. Omran, S. Ramos, et al. , “The cityscapes dataset for semantic urban scene understanding,” in Proc. IEEE Conf. on Comput. Vis. Pattern Recognit. , 2016

2016

[5] [2]

Vision meets robotics: The kitti dataset,

A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” Int. J. Robot. Res. , 2013

2013

[6] [3]

Bdd100k: A diverse driving video database with scalable annotation tooling,

F. Yu, W. Xian, Y . Chen, et al. , “Bdd100k: A diverse driving video database with scalable annotation tooling,” 2018, arXiv:1805.04687

arXiv 2018

[7] [4]

Generalization by adaptation: Diffusion-based domain extension for domain-generalized semantic segmentation,

J. Niemeijer, M. Schwonberg, J.-A. Termöhlen, N. M. Schmidt, and T. Fingscheidt, “Generalization by adaptation: Diffusion-based domain extension for domain-generalized semantic segmentation,” in IEEE / CVF Comput. Vis. Pattern Recognit. Conf. Workshop, 2024. PREPRINT 17

2024

[8] [5]

Survey on unsupervised domain adaptation for semantic segmentation for visual perception in automated driving,

M. Schwonberg, J. Niemeijer, J.-A. Termöhlen, N. M. Schmidt, H. Gottschalk, T. Fingscheidt, et al. , “Survey on unsupervised domain adaptation for semantic segmentation for visual perception in automated driving,” IEEE Access , 2023

2023