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REVIEW 4 major objections 5 minor 44 references

Automatically Generating High-Precision Simulated Road Networking in Traffic Scenario

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read The paper claims that lane-level simulation road networks can be generated fully automatically from street-view imagery and open-source road topology, replacing manual map editing.

desk verdict A plausible street-view-to-simulation-network pipeline, but the central 'high-precision' claim rests on an unexplained coordinate transformation and zero quantitative evaluation. read the letter →

arxiv 2509.02990 v1 pith:N64MFZH7 submitted 2025-09-03 cs.MM

Liang Xie , Wenke Huang This is my paper · ORCID
classification cs.MM
keywords lane-levelroadnetworkgenerationtrafficsimulationstreetviewimagerylanelinedetectionCNN-TransformermapmatchingFréchetdistancevectortopology
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 argues that lane-level simulation road networks—the detailed digital maps traffic simulators and autonomous-driving tests depend on—can be built without manual editing. The proposed pipeline pulls street-view images and open-source vector road topology, detects lane lines with an end-to-end neural network, back-projects those detections onto the ground plane, and matches them onto the road network using a curve-similarity algorithm. The authors demonstrate a city-wide reconstruction for Shenzhen and claim the result closely matches real street conditions. If right, this would cut the cost and time of producing simulation road networks dramatically.

What carries the argument

The load-bearing mechanism is a back-projection-and-map-matching fusion of two data sources. A CNN-Transformer network—a hybrid of convolutional and attention layers—predicts lane shapes as an unordered set, trained end-to-end with a Hungarian loss that assigns each prediction to one ground-truth lane without non-maximum suppression. The predicted lane lines are then converted from image coordinates to geographic coordinates and matched onto the base vector road network using the discrete Fréchet distance, a curve-similarity measure that finds the best trajectory-like correspondence between detected lane markings and the known road centerlines.

What would settle it

Pick a hilly road section or a multi-level interchange where ground-truth lane positions are known from survey or lidar. Run the complete pipeline on street-view images for that section. If the projected lane polylines deviate from truth by more than one lane width, or the Fréchet matcher snaps to the wrong road level, the central claim fails for non-planar roads; the same test on flat grid streets should pass if the claim holds.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that a lane-level traffic-simulation road network can be synthesized automatically from two widely available sources: street-view photographs and open-source map topology. A CNN-Transformer detection model directly regresses lane shape parameters, including geometry, approximate curvature, and camera pose, and is trained end-to-end with a Hungarian matching loss. The detected lane polylines are transformed into geographic coordinates and fused with the base road network through a Fréchet-distance map-matching algorithm. The output is a vectorized road network containing topology, node connectivity, and turn connectivity, which the authors show recon

Load-bearing premise

The whole pipeline assumes that a single street-view image, with only its recorded camera location, can be back-projected onto the road plane accurately enough that detected lane lines end up in the right geographic position; non-planar roads or imprecise camera geotags break this.

Editorial extensions

If this is right

  • A city's lane-level simulation road network could be rebuilt in hours rather than months of manual post-editing, using only imagery and open map data.
  • The same pipeline could refresh an existing digital road network as street-view imagery is updated, keeping traffic simulations aligned with real changes.
  • Because the output includes node connectivity and turn connectivity, it can be imported directly into traffic simulators for signal control and dynamic traffic assignment studies.
  • The approach removes the dependence on expensive lidar or point-cloud surveys for lane-level detail, lowering the barrier for smaller cities and campuses.

Reading between the lines

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

  • The accuracy ceiling is set by the single-image back-projection: bridges, ramps, and hilly roads violate the locally planar ground assumption, so the pipeline would likely need multi-view or depth information there—the paper does not address this.
  • Because the matcher snaps detections onto an existing open-map topology, any road absent or outdated in that base map cannot be created by the lane detector alone; combining aerial imagery could patch such gaps.
  • The constructed multi-lane street-view dataset, covering more than ten lanes and negative samples, is itself a reusable asset that may transfer to other cities served by the same street-view provider.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes an automated pipeline for generating lane-level simulation road networks from street view imagery and open-source road topology. The workflow consists of collecting Baidu Street View data, constructing a lane-line annotation dataset, training a CNN-Transformer lane detection model, and then applying a described-but-unspecified 'coordinate transformation' and a Fréchet-based map matching algorithm to fuse detected lane polylines with OpenStreetMap centerlines. The authors claim that the result is a high-precision, fully automated, efficient lane-level road network. The evaluation is limited to qualitative figures showing sample detections, local overlays, and city-scale visualizations; no numerical accuracy, runtime, or cost metrics are reported.

Significance. If substantiated, the approach would address a real bottleneck in traffic simulation and autonomous driving: the labor-intensive construction of lane-level road networks. The emphasis on a diverse lane-line dataset and the use of open map data are also timely. However, as written, the central claim of 'high-precision' generation is not established. The critical step that maps image-space lane detections to geographic coordinates is not specified in any reproducible form, and the evaluation contains no quantitative support. The high-level idea is plausible, but the manuscript lacks the technical content and validation required for publication in a serious journal.

major comments (4)
  1. [Section 2.3 / Abstract] The core step of the pipeline is described only as 'coordinate transformation' (also in the abstract). No equations, camera model, intrinsics/extrinsics, ground-plane assumption, or error analysis are provided. Section 2.2's assertion that the network 'approximates road curvature and camera pose through explicit mathematical formulations' is not accompanied by any such formulation. Without a specification of how image-space lane detections are projected to geographic coordinates, the 'high-precision lane-level' output cannot be assessed or reproduced.
  2. [Section 2.3 / Figures 9-11] The evaluation is entirely qualitative. There are no lane detection accuracy numbers (e.g., IoU, F1, lane-count error), no comparison against a ground-truth lane-level map, no map-matching error metrics, and no runtime or cost measurements. The statements 'demonstrating that our method produces highly accurate reconstructions' (Fig. 9) and 'high-precision simulation' (Fig. 10) are not supported by any quantitative evidence. The claim of 'high efficiency and speed' in Section 2.3 is likewise unquantified.
  3. [Section 2.3 / Figure 7] The Fréchet map matching can align detected lane polylines to OSM road centerlines, but OSM contains no lane geometry. If the coordinate transformation introduces a systematic lateral offset, or if the road surface is non-planar (hills, overpasses, banked curves), the OSM matching cannot repair that error. The paper states neither the required accuracy of camera pose/geotags nor the planar-road assumption, and it provides no failure analysis. This is a load-bearing gap in the claimed precision.
  4. [Sections 2.1-2.2] The dataset is called 'large-scale' but no size, class distribution, or train/test split is given, and the network architecture and the regressed parameterization are not specified. Figure 6 claims 'superior accuracy and robustness' compared with conventional CNNs, but no quantitative comparison is provided. These omissions prevent independent verification of the detection stage, which is the input to the rest of the pipeline.
minor comments (5)
  1. [Section 2.1] Typo: 'shwon' should be 'shown'.
  2. [References] Reference [7] appears duplicated in the same citation group; several bibliography entries have formatting problems (e.g., 'InProceedings', missing spaces). The reference list also contains many domain-unrelated entries (e.g., agronomy, weed recognition, cytology), which weakens the scholarly apparatus.
  3. [General] The manuscript contains no equations, algorithmic pseudocode, or data availability statement. Adding these would substantially aid reproducibility.
  4. [Figures 8 and 11] City-scale network figures would benefit from scale bars, coordinate grids, and an overlay against a ground-truth lane-level map; currently they serve only as anecdotal illustrations.
  5. [Section 3] The concluding section claims 'superior performance' and 'high accuracy' but no supporting experimental section exists. The paper should include an explicit evaluation section with defined metrics.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the pipeline is an empirical system with an underspecified coordinate-transform step, not a self-referential derivation.

full rationale

I walked the paper's claimed derivation chain: data collection, dataset construction, CNN-Transformer lane detection, coordinate transformation, and map matching to OSM topology. No equation in the paper defines the output in terms of the input in a way that would make a 'prediction' identical to a fitted parameter or to a cited prior result. The lane detector is trained end-to-end on a private annotated dataset, which is standard supervised learning rather than circular reasoning. The Frachet-based map matching to OSM is a post-processing alignment step; it does not by construction force the detected lane geometry to equal the OSM centerline, since OSM provides only road topology and not lane-level geometry. The paper's self-citations (e.g., refs. [27], [28], [29]) are not load-bearing for the central pipeline and no uniqueness theorem is imported from the authors' prior work. The genuine weakness identified by the skeptic and by the reader's take is that Section 2.3's 'coordinate transformation' is not specified with equations, camera intrinsics, pose estimation, or ground-plane assumptions, so the 'high-precision' claim is not quantitatively supported. That is a validation and reproducibility gap, not a circularity: an unspecified step cannot reduce to its own input unless the paper defines it that way, and it does not. No external benchmark is used, and the evaluation is qualitative, but the absence of quantitative validation is a separate correctness concern rather than evidence that the derivation is circular. Therefore the appropriate finding is no significant circularity, score 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the trained lane detection model, the accuracy of street view geotags, the correctness of OSM topology, and the validity of the coordinate transformation and Frechet matching. None of these is validated quantitatively in the paper, and no artifacts are released.

free parameters (2)
  • Lane detection model weights = not provided
    The CNN-Transformer model is trained end-to-end on a private dataset; its learned weights are the primary free parameters, but no checkpoint or training details are released.
  • Frechet matching threshold and coordinate transformation parameters = not specified
    Section 2.3 invokes a map matching algorithm and coordinate transformation without stating thresholds, camera intrinsics, or ground-plane parameters on which the fusion accuracy depends.
assumptions (4)
  • domain assumption Street view images from Baidu Street View have sufficiently accurate geotags (location and capture time).
    Section 2.1 says location and capture time are recorded for traceability. If geotags are imprecise, the coordinate transformation and map matching inherit positional error, and the generated network cannot be high-precision.
  • domain assumption OpenStreetMap (or Baidu Maps) road topology is accurate and complete enough to serve as the base network.
    Figure 3 and Section 2.3 treat OSM as the foundational topology. Lane-level refinement cannot correct missing or misaligned road centerlines.
  • domain assumption The Frechet distance trajectory comparison correctly associates each detected lane to the corresponding road segment.
    Section 2.3 relies on Frechet matching between detected lane paths and the base vector network. This assumes lanes and road centerlines are comparable as trajectories and that the best match is the correct one.
  • domain assumption The constructed lane line dataset is representative enough for the trained model to generalize across all of Shenzhen.
    Section 2.1 claims diversity of scenarios, but no dataset statistics are given, and no held-out evaluation across districts is reported.

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Cite this review

Pith. "Pith review of Automatically Generating High-Precision Simulated Road Networking in Traffic Scenario." pith.science (2026). https://pith.science/paper/N64MFZH7

@misc{pith2026250902990,
  author       = {Pith},
  title        = {Pith review of: Automatically Generating High-Precision Simulated Road Networking in Traffic Scenario},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N64MFZH7}},
  note         = {Machine review of arXiv:2509.02990}
}
read the original abstract

Existing lane-level simulation road network generation is labor-intensive, resource-demanding, and costly due to the need for large-scale data collection and manual post-editing. To overcome these limitations, we propose automatically generating high-precision simulated road networks in traffic scenario, an efficient and fully automated solution. Initially, real-world road street view data is collected through open-source street view map platforms, and a large-scale street view lane line dataset is constructed to provide a robust foundation for subsequent analysis. Next, an end-to-end lane line detection approach based on deep learning is designed, where a neural network model is trained to accurately detect the number and spatial distribution of lane lines in street view images, enabling automated extraction of lane information. Subsequently, by integrating coordinate transformation and map matching algorithms, the extracted lane information from street views is fused with the foundational road topology obtained from open-source map service platforms, resulting in the generation of a high-precision lane-level simulation road network. This method significantly reduces the costs associated with data collection and manual editing while enhancing the efficiency and accuracy of simulation road network generation. It provides reliable data support for urban traffic simulation, autonomous driving navigation, and the development of intelligent transportation systems, offering a novel technical pathway for the automated modeling of large-scale urban road networks.

Figures

Figures reproduced from arXiv: 2509.02990 by the authors.

Figure 1
Figure 1. Can a road network be reconstructed from [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Automatic generation of lane-level urban simulation road networks based on street view. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. The base vector map data is sourced from [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: The lane detection dataset presented in this [PITH_FULL_IMAGE:figures/full_fig_p003_5.png]
Figure 7
Figure 7. Figure 7: The trajectory similarity algorithm is im [PITH_FULL_IMAGE:figures/full_fig_p004_7.png]
Figure 10
Figure 10. Figure 10: High-precision simulation of detailed local [PITH_FULL_IMAGE:figures/full_fig_p005_10.png]
Figure 9
Figure 9. Figure 9: The distribution of the street view image lay [PITH_FULL_IMAGE:figures/full_fig_p005_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

44 extracted references · 44 canonical work pages

  1. [1]

    Jiping Bi, Yongchao Song, Yahong Jiang, Lijun Sun, Xuan Wang, Zhaowei Liu, Jindong Xu, Siwen Quan, Zhe Dai, and Weiqing Yan

    work page

  2. [2]

    Karl Bringmann, Marvin Künnemann, and André Nusser. 2021. Dis- crete Fréchet distance under translation: Conditional hardness and an improved algorithm. ACM Transactions on Algorithms 17, 3 (2021), 1–42

    work page 2021

  3. [3]

    Liying Cao, Miao Sun, Zhicheng Yang, Donghui Jiang, Dongjie Yin, and Yunpeng Duan. 2024. A novel transformer-CNN approach for pre- dicting soil properties from LUCAS Vis-NIR spectral data. Agronomy 14, 9 (2024), 1998

    work page 2024

  4. [4]

    Jinrong Cui, Youliu Zhang, Hao Chen, Yaoxuan Zhang, Hao Cai, Yu Jiang, Ruijun Ma, and Long Qi. 2025. CSWin-MBConv: A dual-network fusing CNN and Transformer for weed recognition. European Journal of Agronomy 164 (2025), 127528

    work page 2025

  5. [5]

    Ahmed M Elshewey. 2024. Orthopedic disease classification based on breadth-first search algorithm. Scientific Reports 14, 1 (2024), 23368

    work page 2024

  6. [6]

    Yunping Fang, Stergios-Aristoteles Mitoulis, Daniel Boddice, Jialiang Yu, and Jelena Ninic. 2025. Scan-to-BIM-to-Sim: Automated recon- struction of digital and simulation models from point clouds with applications on bridges. Results in Engineering 25 (2025), 104289

    work page 2025

  7. [7]

    Sa Gao, Qingsong Ran, Zicheng Su, Ling Wang, Wanjing Ma, and Ruochen Hao. 2024. Evaluation system for urban traffic intelligence based on travel experiences: A sentiment analysis approach. Trans- portation Research Part A: Policy and Practice 187 (2024), 104170

    work page 2024

  8. [8]

    Meiling Gong, Dong Wang, Xiaoxia Zhao, Huimin Guo, Donghao Luo, and Min Song. 2021. A review of non-maximum suppression algorithms for deep learning target detection. InSeventh Symposium on Novel Photoelectronic Detection Technology and Applications, Vol. 11763. SPIE, 821–828

    work page 2021

Show all 44 references
  1. [9]

    Dan Li, Zan Yang, Wei Nai, Yidan Xing, and Ziyu Chen. 2025. A road lane detection approach based on reformer model.Egyptian Informatics Journal 29 (2025), 100625

    2025

  2. [10]

    Yong Li, Naipeng Miao, Liangdi Ma, Feng Shuang, and Xingwen Huang

  3. [11]

    Yueyuan Li, Wei Yuan, Songan Zhang, Weihao Yan, Qiyuan Shen, Chunxiang Wang, and Ming Yang. 2024. Choose your simulator wisely: A review on open-source simulators for autonomous driving. IEEE Transactions on Intelligent Vehicles (2024)

    2024

  4. [12]

    Huei-Yung Lin, Chun-Ke Chang, et al. 2024. Lane detection networks based on deep neural networks and temporal information. Alexandria Engineering Journal 98 (2024), 10–18

    2024

  5. [13]

    Ruijin Liu, Zejian Yuan, Tie Liu, and Zhiliang Xiong. 2021. End-to-end lane shape prediction with transformers. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . 3694–3702

    2021

  6. [14]

    Fangfang Lu, Guxue Sun, Huiqun Yu, Yijie Huang, Tong Zhou, and Sangyu Yao. 2025. LPCNet: End-to-end lane detection with PnP com- pression and condition DETR. Displays 87 (2025), 102902

    2025

  7. [15]

    Antonios Makris, Konstantinos Tserpes, Giannis Spiliopoulos, Dim- itrios Zissis, and Dimosthenis Anagnostopoulos. 2021. MongoDB Vs PostgreSQL: A comparative study on performance aspects. GeoInfor- matica 25 (2021), 243–268

    2021

  8. [16]

    Johannes Nguyen. 2021. An overview of agent-based traffic simulators. Transportation research interdisciplinary perspectives 12 (2021), 100486

    2021

  9. [17]

    Maximilian Pittner, Joel Janai, and Alexandru P Condurache. 2024. Lanecpp: Continuous 3d lane detection using physical priors. In Pro- ceedings of the IEEE/CVF Conference on computer vision and pattern recognition. 10639–10648

    2024

  10. [18]

    Hongye Quan, Wanli Ni, Tong Zhang, Xiangyu Ye, Ziyi Xie, Shuai Wang, Yuanwei Liu, and Hui Song. 2025. Large language model agents for radio map generation and wireless network planning. IEEE Net- working Letters (2025)

    2025

  11. [19]

    KM Karthick Raghunath, C Rohith Bhat, Venkatesan Vinoth Kumar, Velmurugan Athiyoor Kannan, TR Mahesh, K Manikandan, and N Krishnamoorthy. 2024. Redefining urban traffic dynamics with TCN- FL driven traffic prediction and control strategies. IEEE Access 12 (2024), 115386–115399

    2024

  12. [20]

    Hao Ren, Mingwei Wang, Yanyang Deng, Wenping Li, and Chen Liu

  13. [21]

    I-Chen Sang and William R Norris. 2024. A robust lane detection algorithm adaptable to challenging weather conditions. IEEE Access 12 (2024), 11185–11195

    2024

  14. [22]

    Jigang Tang, Songbin Li, and Peng Liu. 2021. A review of lane detec- tion methods based on deep learning. Pattern Recognition 111 (2021), 107623

    2021

  15. [23]

    Engi- neering Applications of Artificial Intelligence 152 (2025), 110585

    Layer-wise feature refinement for accurate three-dimensional lane detection with enhanced bird’s eye view transformation. Engi- neering Applications of Artificial Intelligence 152 (2025), 110585

    2025

  16. [24]

    Atsushi Teramoto, Ayano Michiba, Yuka Kiriyama, Tetsuya Tsukamoto, Kazuyoshi Imaizumi, and Hiroshi Fujita. 2025. Automated description generation of cytologic findings for lung cytological images using a pretrained vision model and dual text decoders: preliminary study. Cytopa...

    2025

  17. [25]

    Hayri Ulvi, Mehmet Akif Yerlikaya, and Kürşat Yildiz. 2024. Urban traffic mobility optimization model: A novel mathematical approach for predictive urban traffic analysis. Applied Sciences 14, 13 (2024), 5873

    2024

  18. [26]

    Yun Tang. 2022. Automatic map generation for autonomous driving system testing. arXiv preprint arXiv:2206.09357 (2022)

    2022 arXiv

  19. [27]

    Liang Xie and Songlin Fan. 2025. A Learning-based Multi-Frame Visual Feature Framework for Real-Time Driver Fatigue Detection. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technolo...

    2025

  20. [28]

    Liang Xie, MengHao Hu, and XinBei Bai. 2022. Online Improved Vehicle Tracking Accuracy via Unsupervised Route Generation. In IEEE 34th International Conference on Tools with Artificial Intelligence . IEEE, 788–792

    2022

  21. [29]

    Zhangyu Wang, Zhihao Liao, Bin Zhou, Guizhen Yu, and Wenwen Luo

  22. [30]

    Fulin Xu, Shaohui Mei, Ge Zhang, Nan Wang, and Qian Du. 2024. Bridging CNN and transformer with cross-attention fusion network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 62 (2024), 1–14

    2024

  23. [31]

    Junjie Yang, Haibo Wan, and Zhihai Shang. 2025. Enhanced hybrid CNN and transformer network for remote sensing image change de- tection. Scientific Reports 15, 1 (2025), 10161

    2025

  24. [32]

    Jiawei Yao, Xiaochao Pan, Tong Wu, and Xiaofeng Zhang. 2024. Build- ing lane-level maps from aerial images. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 3890–3894

    2024

  25. [33]

    Liang Xie, MengHao Hu, and XinBei Bai. 2022. Towards Hardware- Friendly and Robust Facial Landmark Detection Method. In Interna- tional Conference on Neural Information Processing . Springer, 432–444

    2022

  26. [34]

    Feng You, Yi Xie, Siyi Zhang, Hao Chen, Haiwei Wang, Wei Zhang, and Jianrong Liu. 2025. Attention based network for real-time road drivable area, lane line detection and scene identification. Engineering Applications of Artificial Intelligence 160 (2025), 111781

    2025

  27. [35]

    Xinyang Yu, Younggu Her, Wenqian Huo, Guowei Chen, and Wei Qi. 2022. Spatio-temporal monitoring of urban street-side vegetation greenery using Baidu Street View images. Urban Forestry & Urban Greening 73 (2022), 127617

    2022

  28. [36]

    Noor Jannah Zakaria, Mohd Ibrahim Shapiai, Rasli Abd Ghani, Mohd Najib Mohd Yassin, Mohd Zamri Ibrahim, and Nurbaiti Wahid. 2023. Lane detection in autonomous vehicles: A systematic review. IEEE access 11 (2023), 3729–3765

    2023

  29. [37]

    Wenjian Yao, Jiajun Bai, Wei Liao, Yuheng Chen, Mengjuan Liu, and Yao Xie. 2024. From cnn to transformer: A review of medical image segmentation models. Journal of Imaging Informatics in Medicine 37, 4 (2024), 1529–1547

    2024

  30. [38]

    Huaikun Zhang, Jing Lian, and Yide Ma. 2025. FET-UNet: Merging CNN and transformer architectures for superior breast ultrasound image segmentation. Physica Medica 133 (2025), 104969

    2025

  31. [39]

    Youcheng Zhang, Zongqing Lu, Xuechen Zhang, Jing-Hao Xue, and Qingmin Liao. 2021. Deep learning in lane marking detection: A survey. IEEE Transactions on Intelligent Transportation Systems 23, 7 (2021), 5976–5992

    2021

  32. [40]

    Ziyuan Zhong, Davis Rempe, Danfei Xu, Yuxiao Chen, Sushant Veer, Tong Che, Baishakhi Ray, and Marco Pavone. 2023. Guided condi- tional diffusion for controllable traffic simulation. InIEEE International Conference on Robotics and Automation . IEEE, 3560–3566

    2023

  33. [41]

    Yunfei Zha, Jianxian Deng, Yinyuan Qiu, Kun Zhang, and Yanyan Wang. 2023. A survey of intelligent driving vehicle trajectory track- ing based on vehicle dynamics. SAE International journal of vehicle dynamics, stability, and NVH 7, 10-07-02-0014 (2023), 221–248

    2023

  34. [2023]

    Engi- neering Applications of Artificial Intelligence 126 (2023), 107021

    Transformer for object detection: Review and benchmark. Engi- neering Applications of Artificial Intelligence 126 (2023), 107021

    2023

  35. [2024]

    IEEE Transactions on Instrumentation and Measurement (2024)

    SwinURNet: Hybrid transformer-cnn architecture for real-time unstructured road segmentation. IEEE Transactions on Instrumentation and Measurement (2024)

    2024

  36. [2025]

    IEEE Transactions on Intelligent Transportation Systems (2025)

    Lane detection for autonomous driving: Comprehensive re- views, current challenges, and future predictions. IEEE Transactions on Intelligent Transportation Systems (2025)

    2025

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Reviewed August 5, 2026 · model on record in the stance chip above.