REVIEW 13 cited by
DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
End-to-end autonomous driving (E2E-AD) has emerged as a trend in the field of autonomous driving, promising a data-driven, scalable approach to system design. However, existing E2E-AD methods usually adopt the sequential paradigm of perception-prediction-planning, which leads to cumulative errors and training instability. The manual ordering of tasks also limits the system`s ability to leverage synergies between tasks (for example, planning-aware perception and game-theoretic interactive prediction and planning). Moreover, the dense BEV representation adopted by existing methods brings computational challenges for long-range perception and long-term temporal fusion. To address these challenges, we present DriveTransformer, a simplified E2E-AD framework for the ease of scaling up, characterized by three key features: Task Parallelism (All agent, map, and planning queries direct interact with each other at each block), Sparse Representation (Task queries direct interact with raw sensor features), and Streaming Processing (Task queries are stored and passed as history information). As a result, the new framework is composed of three unified operations: task self-attention, sensor cross-attention, temporal cross-attention, which significantly reduces the complexity of system and leads to better training stability. DriveTransformer achieves state-of-the-art performance in both simulated closed-loop benchmark Bench2Drive and real world open-loop benchmark nuScenes with high FPS.
Forward citations
Cited by 13 Pith papers
-
Decaying Turbulence and the Riemann Hypothesis: The number theory behind the infinite-time singularity
Freely decaying incompressible turbulence possesses a universal Euler-ensemble attractor whose continuum Mellin spectrum is controlled by the non-trivial zeros of the Riemann zeta function, producing an infinite-time ...
-
Deconfounded Lifelong Learning for Autonomous Driving via Dynamic Knowledge Spaces
DeLL combines DPMM dual knowledge spaces with front-door causal adjustment and a non-autoregressive evolutionary decoder to reduce catastrophic forgetting and spurious correlations in lifelong end-to-end autonomous driving.
-
WAM-Diff2: Hierarchical AR-to-Diffusion Distillation for Highly Efficient Autonomous Driving VLA
A three-stage distillation converts an autoregressive driving VLA into a block-causal masked diffusion model, preserving planning accuracy while decoding 2.8x faster (15.1x with optimized kernels).
-
Latent-Centroid Steering: Single-Pass Classifier-Free Guidance for Command-Aligned Autonomous Driving
LCS steers a VLA driving model's latent features toward command-specific centroids to improve command following at roughly half the inference cost of two-pass CFG.
-
MOJITO: Modal Joint Learning for Unified End-to-End Autonomous Driving
Block-wise Modal Joint Attention over image, LiDAR, and diffusion action tokens yields 88.9 PDMS / 88.4 EPDMS on NAVSIM without anchors or auxiliary supervision.
-
GeoWorldAD: Geometry World Action Model for Autonomous Driving
Grounding an autonomous-driving action model in ego-aligned multi-scale 3D geometry and latent future-geometry tokens improves NAVSIM closed-loop PDMS/EPDMS over prior geometry- and world-model-based planners.
-
BeyondSight: Object Permanence for End-to-End Autonomous Driving
A sparse-query end-to-end driving model that maintains persistent actor hypotheses across full occlusions, plus a nuScenes extension that supervises and evaluates unobservable actors, cuts planning L2 error and raises...
-
AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning
Trajectory-pattern anchors bridge VLA reasoning and continuous residual flow, yielding 77.28% success rate on Bench2Drive closed-loop driving.
-
Driving Like Yourself: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving
Person2Drive is a new benchmark that generates personalized driving datasets via simulation, quantifies styles with MMD and KL metrics, and adapts E2E-AD models using a style reward framework.
-
AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
Conditioning speed planning on the predicted path and relabeling synthetic cut-ins yields SOTA Bench2Drive scores (DS 89.07, SR 73.18%).
-
CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving
Residual waypoint RL around a frozen VLA prior, scaled via heterogeneous CARLA/H100 infrastructure, raises closed-loop driving score and success rate on longest6 v2 and Bench2Drive.
-
UniDrive-WM: Unified Understanding, Planning and Generation World Model for Autonomous Driving
A unified VLM for autonomous driving that couples trajectory planning with future-frame image generation improves open- and closed-loop planning metrics on Bench2Drive and nuScenes.
-
A Survey on Vision-Language-Action Models for Autonomous Driving
A survey organizes vision-language-action models for autonomous driving into four stages, compares over 20 systems, and catalogs datasets, benchmarks, and open challenges.
Discussion (0). Sign in to comment.