Scilight Press

Author Information

Received: 12 Apr 2026 | Revised: 24 Apr 2026 | Accepted: 09 May 2026 | Published: 21 May 2026

Abstract

Keywords

autonomous driving | safety validation | end-to-end | vision-language-action (VLA)

References 

• 1.

  Yurtsever, E.; Lambert, J.; Carballo, A.; et al. A Survey of Autonomous Driving: Common Practices and Emerging Technologies. IEEE Access 2020, 8, 58443–58469.

• 2.

  Shalev-Shwartz, S.; Shammah, S.; Shashua, A. On a Formal Model of Safe and Scalable Self-Driving Cars. arXiv 2017, arXiv:1708.06374.

• 3.

  Chen, N.; Zhang, S.; Zhou, X.; et al. Fast Robustness Enhancement for Dynamic IIoT Topology with Adaptive Bayesian Learning. IEEE Trans. Mob. Comput. 2025, 24, 10886–10899.

• 4.

  Chen, N.; Qiu, T.; Zhou, X.; et al. LEGO-Motif: Enhancing IoT Topology Robustness with Evolutionary Motif-Based Generation. IEEE Trans. Syst. Man Cybern. Syst. 2026, 1–14.

• 5.

  Raju, V.M.; Gupta, V.; Lomate, S. Performance of Open Autonomous Vehicle Platforms: Autoware and Apollo. In Proceedings of the 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, India, 29–31 March 2019; pp. 1–5.

• 6.

  Chen, L.; Wu, P.; Chitta, K.; et al. End-to-End Autonomous Driving: Challenges and Frontiers. IEEE Trans. Pattern Anal. Mach. Intell. 2024, 46, 10164–10183.

• 7.

  Wang, S.; Zhou, Q.; Wu, K.; et al. Interventional Root Cause Analysis of Failures in Multi-Sensor Fusion Perception Systems. In Proceedings of the Network and Distributed System Security (NDSS) Symposium 2025, San Diego, CA, USA, 24–28 February 2025.

• 8.

  Bojarski, M.; Del Testa, D.; Dworakowski, D.; et al. End to End Learning for Self-Driving Cars. arXiv 2016, arXiv:1604.07316.

• 9.

  Hu, Y.; Yang, J.; Chen, L.; et al. Planning-Oriented Autonomous Driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17–24 June 2023; pp. 17853–17862.

• 10.

  Atakishiyev, S.; Salameh, M.; Yao, H.; et al. Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions. IEEE Access 2024, 12, 101603–101625.

• 11.

  Wang, S.; Zhou, Q.; Wu, K.; et al. REDOUBT: Duo Safety Validation for Autonomous Vehicle Motion Planning. In Proceedings of the The Thirty-Ninth Annual Conference on Neural Information Processing Systems, San Diego, CA, USA, 2–7 December 2025.

• 12.

  De Haan, P.; Jayaraman, D.; Levine, S. Causal Confusion in Imitation Learning. In Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, BC, Canada, 8–14 December 2019.

• 13.

  Codevilla, F.; Santana, E.; L´opez, A.M.; et al. Exploring the Limitations of Behavior Cloning for Autonomous Driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 27 October–2 November 2019; pp. 9329–9338.

• 14.

  Luo, Y.; Zheng, J.; Chen, N.; et al. Taming Language Models for Predicting Stock Price with Causality Guidance. IEEE Trans. Emerg. Top. Comput. Intell. 2026, 1–13. https://doi.org/10.1109/TETCI.2026.3663488.

• 15.

  Mao, J.; Qian, Y.; Ye, J.; et al. GPT-Driver: Learning to Drive with GPT. arXiv 2023, arXiv:2310.01415.

• 16.

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

• 17.

  Sima, C.; Renz, K.; Chitta, K.; et al. DriveLM: Driving with Graph Visual Question Answering. In European Conference on Computer Vision; Springer: Berlin/Heidelberg, Germany, 2024; pp. 256–274.

• 18.

  Liu, H.; Xue,W.; Chen, Y.; et al. A Survey on Hallucination in Large Vision-Language Models. arXiv 2024, arXiv:2402.00253.

• 19.

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

• 20.

  Hu, A.; Russell, L.; Yeo, H.; et al. Gaia-1: A GenerativeWorld Model for Autonomous Driving. arXiv 2023, arXiv:2309.17080.

• 21.

  Wang, X.; Zhu, Z.; Huang, G.; et al. DriveDreamer: Towards Real-World-Drive World Models for Autonomous Driving. In European Conference on Computer Vision, Proceedings of the 18th European Conference, Milan, Italy, 29 September–4 October 2024; Springer: Berlin/Heidelberg, Germany, 2024; pp. 55–72.

• 22.

  Kato, S.; Tokunaga, S.; Maruyama, Y.; et al. Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems. In Proceedings of the 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS), Porto, Portugal, 11–13 April 2018; pp. 287–296.

• 23.

  Lang, A.H.; Vora, S.; Caesar, H.; et al. PointPillars: Fast Encoders for Object Detection from Point Clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 12697–12705.

• 24.

  Liu, Z.; Tang, H.; Amini, A.; et al. BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation. In Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), London, UK, 29 May–2 June 2023; pp. 2774–2781.

• 25.

  Huang, Z.; Liu, H.; Lv, C. GameFormer: Game-Theoretic Modeling and Learning of Transformer-Based Interactive Prediction and Planning for Autonomous Driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France, 1–6 October 2023; pp. 3903–3913.

• 26.

  Xin, R.; Cheng, J.; Liu, H.; et al. PlanScope: Learning to Plan Within Decision Scope for Urban Autonomous Driving. IEEE Robot. Autom. Lett. 2026, 11, 3246–3253.

• 27.

  Jiang, B.; Chen, S.; Xu, Q.; et al. VAD: Vectorized Scene Representation for Efficient Autonomous Driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France, 1–6 October 2023; pp. 8340–8350.

• 28.

  Yang, J.; Gao, S.; Qiu, Y.; et al. GenAD: Generalized Predictive Model for Autonomous Driving. arXiv 2024, arXiv:2403.09630.

• 29.

  Sun, W.; Lin, X.; Shi, Y.; et al. SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation. In Proceedings of the 2025 IEEE International Conference on Robotics and Automation (ICRA), Atlanta, GA, USA, 19–23 May 2025; pp. 8795–8801.

• 30.

  Chen, L.; Li, Y.; Huang, C.; et al. Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys. IEEE Trans. Intell. Veh. 2022, 8, 1046–1056.

• 31.

  Xu, Z.; Zhang, Y.; Xie, E.; et al. DriveGPT4: Interpretable End-to-End Autonomous Driving via Large Language Model. IEEE Robot. Autom. Lett. 2024, 9, 8186–8193.

• 32.

  Ma, Y.; Wei, T.; Zhong, N.; et al. LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking. IEEE Trans. Neural Netw. Learn. Syst. 2025, 37, 1963–1977.

• 33.

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

• 34.

  Peri, N.; Dave, A.; Ramanan, D.; et al. Towards Long-Tailed 3D Detection. In Proceedings of The 6th Conference on Robot Learning; Auckland, New Zealand, 14–18 December 2022; pp. 1904–1915.

• 35.

  Dong, Y.; Kang, C.; Zhang, J.; et al. Benchmarking Robustness of 3D Object Detection to Common Corruptions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17–24 June 2023; pp. 1022–1032.

• 36.

  Gu, Y.; Wang, Q.; Qin, X. Real-Time Streaming Perception System for Autonomous Driving. In Proceedings of the 2021 China Automation Congress (CAC), Beijing, China, 22–24 October 2021; pp. 5239–5244.

• 37.

  Sakaridis, C.; Dai, D.; Van Gool, L. ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 10–17 October 2021; pp. 10765–10775.

• 38.

  Cao, Y.; Wang, N.; Xiao, C.; et al. Invisible for Both Camera and LiDAR: Security of Multi-Sensor Fusion Based Perception in Autonomous Driving Under Physical-World Attacks. In Proceedings of the 2021 IEEE Symposium on Security and Privacy (SP), online, 24–27 May 2021; pp. 176–194.

• 39.

  Tu, J.; Ren, M.; Manivasagam, S.; et al. Physically Realizable Adversarial Examples for LiDAR Object Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 13716–13725.

• 40.

  Trautman, P.; Krause, A. Unfreezing the Robot: Navigation in Dense, Interacting Crowds. In Proceedings of them 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, 18–22 October 2010; pp. 797–803.

• 41.

  Cheng, J.; Chen, Y.; Chen, Q. Pluto: Pushing the Limit of Imitation Learning-Based Planning for Autonomous Driving. arXiv 2024, arXiv:2404.14327.

• 42.

  Suo, S.; Regalado, S.; Casas, S.; et al. TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 10400–10409.

• 43.

  Xie, S.; Kong, L.; Dong, Y.; et al. Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data and Metric Perspectives. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Honolulu, HI, USA, 19–20 October 2025; pp. 6585–6597.

• 44.

  NVIDIA;Wang, Y.; Luo,W.; et al. Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail. arXiv 2025, arXiv:2511.00088.

• 45.

  Sun, P.; Kretzschmar, H.; Dotiwalla, X.; et al. Scalability in Perception for Autonomous Driving: Waymo Open Dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 2446–2454.

• 46.

  Caesar, H.; Bankiti, V.; Lang, A.H.; et al. nuScenes: A Multimodal Dataset for Autonomous Driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–June 19 2020; pp. 11621–11631.

• 47.

  Zhong, Z.; Rempe, D.; Chen, Y.; et al. Language-Guided Traffic Simulation via Scene-Level Diffusion. In Proceedings of The 7th Conference on Robot Learning, Atlanta, GA, USA, 6–9 November 2023; pp. 144–177.

• 48.

  Sun, C.; Zhang, R.; Lu, Y.; et al. Toward Ensuring Safety for Autonomous Driving Perception: Standardization Progress, Research Advances, and Perspectives. IEEE Trans. Intell. Transp. Syst. 2024, 25, 3286–3304.

• 49.

  Teng, S.; Hu, X.; Deng, P.; et al. Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives. IEEE Trans. Intell. Veh. 2023, 8, 3692–3711.

• 50.

  Jiang, S.; Huang, Z.; Qian, K.; et al. A Survey on Vision-Language-Action Models for Autonomous Driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Honolulu, HI, USA, 19–20 October 2025; pp. 4524–4536.

• 51.

  Yang, Z.; Jia, X.; Li, H.; et al. LLM4Drive: A Survey of Large Language Models for Autonomous Driving. arXiv 2023, arXiv:2311.01043.

• 52.

  Bai, X.; Hu, Z.; Zhu, X.; et al. TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022; pp. 1090–1099.

• 53.

  Ge, C.; Chen, J.; Xie, E.; et al. MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation. arXiv 2023, arXiv:2304.09801.

• 54.

  Gal, Y.; Ghahramani, Z. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. In Proceedings of The 33rd International Conference on Machine Learning, New York, NY, USA, 20–22 June 2016; pp. 1050–1059.

• 55.

  Lakshminarayanan, B.; Pritzel, A.; Blundell, C. Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles. In Proceedings of the 31st International Conference on Neural Information Processing Systems, Red Hook, NY, USA, 4–9 December 2017; pp. 6405–6416.

• 56.

  Miller, D.; Nicholson, L.; Dayoub, F.; et al. Dropout Sampling for Robust Object Detection in Open-Set Conditions. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 21–25 May 2018; pp. 3243–3249.

• 57.

  Sensoy, M.; Kaplan, L.; Kandemir, M. Evidential Deep Learning to Quantify Classification Uncertainty. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montr´eal, QC, Canada, 3–8 December 2018; pp. 3183–3193.

• 58.

  Amini, A.; Schwarting, W.; Soleimany, A.; et al. Deep Evidential Regression. Adv. Neural Inf. Process. Syst. 2020, 33, 14927–14937.

• 59.

  Guo, C.; Pleiss, G.; Sun, Y.; et al. On Calibration of Modern Neural Networks. In Proceedings of the 34th International Conference on Machine Learning, Sydney, NSW, Australia, 6–11 August 2017; pp. 1321–1330.

• 60.

  Xie, S.; Kong, L.; Zhang, W.; et al. RoboBEV: Towards Robust Bird’s Eye View Perception Under Corruptions. arXiv 2023, arXiv:2304.06719.

• 61.

  Philion, J.; Fidler, S. Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D. In Proceedings of the 16th European Conference, Glasgow, UK, 23–28 August 2020; pp. 194–210.

• 62.

  Ivanovic, B.; Pavone, M. The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Spatiotemporal Graphs. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 27 October–2 November 2019; pp. 2375–2384.

• 63.

  Reading, C.; Harakeh, A.; Chae, J.; et al. Categorical Depth Distribution Network for Monocular 3D Object Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 8555–8564.

• 64.

  Hecker, S.; Dai, D.; Van Gool, L. End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 435–453.

• 65.

  Ishihara, K.; Kanervisto, A.; Miura, J.; et al. Multi-Task Learning with Attention for End-to-End Autonomous Driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 19–25 June 2021; pp. 2902–2911.

• 66.

  Hu, S.; Chen, L.; Wu, P.; et al. ST-P3: End-to-End Vision-Based Autonomous Driving via Spatial-Temporal Feature Learning. In Proceedings of the 17th European Conference on Computer Vision, Tel Aviv, Israel, 23–27 October 2022; pp. 533–549.

• 67.

  Ren, X.; Yang, T.; Li, L.E.; et al. Safety-AwareMotion Prediction with Unseen Vehicles for Autonomous Driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 10–17 October 2021; pp. 15731–15740.

• 68.

  Zablocki, E.; Ben-Younes, H.; P´erez, P.; et al. Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges. Int. J. Comput. Vis. 2022, 130, 2425–2452.

• 69.

  Cultrera, L.; Seidenari, L.; Becattini, F.; et al. Explaining Autonomous Driving by Learning End-to-End Visual Attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA, 14–19 June 2020; pp. 340–341.

• 70.

  Kim, J.; Rohrbach, A.; Darrell, T.; et al. Textual Explanations for Self-Driving Vehicles. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 563–578.

• 71.

  Sadat, A.; Casas, S.; Ren, M.; et al. Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations. In Proceedings of the European Conference on Computer Vision; Glasgow, UK, 23-28 August 2020; pp. 414–430.

• 72.

  Chen, D.; Zhou, B.; Koltun, V.; et al. Learning by Cheating. In Proceedings of the Conference on Robot Learning, online, 16–18 November 2020; pp. 66–75.

• 73.

  Zeng, W.; Luo, W.; Suo, S.; et al. End-to-End Interpretable Neural Motion Planner. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 8660–8669.

• 74.

  Altman, E. Constrained Markov Decision Processes; Routledge: Oxfordshire, UK, 2021.

• 75.

  Achiam, J.; Held, D.; Tamar, A.; et al. Constrained Policy Optimization. In Proceedings of the 34 th International Conference on Machine Learning, Sydney, NSW, Australia, 6–11 August 2017; pp. 22–31.

• 76.

  Tessler, C.; Mankowitz, D.J.; Mannor, S. Reward Constrained Policy Optimization. arXiv 2018, arXiv:1805.11074.

• 77.

  Isele, D.; Nakhaei, A.; Fujimura, K. Safe Reinforcement Learning on Autonomous Vehicles. In Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 1–5 October 2018; pp. 1–6.

• 78.

  Alshiekh, M.; Bloem, R.; Ehlers, R.; et al. Safe Reinforcement Learning via Shielding. In Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA, 2–7 February 2018; pp. 2669–2678.

• 79.

  Cheng, R.; Orosz, G.; Murray, R.M.; et al. End-to-End Safe Reinforcement Learning Through Barrier Functions for Safety-Critical Continuous Control Tasks. In Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA, 27 January–1 February 2019; pp. 3387–3395.

• 80.

  Goodfellow, I.J.; Shlens, J.; Szegedy, C. Explaining and Harnessing Adversarial Examples. arXiv 2014, arXiv:1412.6572.

• 81.

  Eykholt, K.; Evtimov, I.; Fernandes, E.; et al. Robust Physical-World Attacks on Deep Learning Visual Classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 1625–1634.

• 82.

  Madry, A.; Makelov, A.; Schmidt, L.; et al. Towards Deep Learning Models Resistant to Adversarial Attacks. arXiv 2017, arXiv:1706.06083.

• 83.

  Tobin, J.; Fong, R.; Ray, A.; et al. Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World. In Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada, 24–28 September 2017; pp. 23–30.

• 84.

  Zhou, Z.; Cai, T.; Zhao, S.Z.; et al. AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning. Adv. Neural Inf. Process. Syst. 2026, 38, 27920–27956.

• 85.

  Wang, D.; Shelhamer, E.; Liu, S.; et al. Tent: Fully Test-TimeAdaptation by EntropyMinimization. arXiv 2020, arXiv:2006.10726.

• 86.

  Hendrycks, D.; Gimpel, K. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. arXiv 2016, arXiv:1610.02136.

• 87.

  Li, Y.; Du, Y.; Zhou, K.; et al. Evaluating Object Hallucination in Large Vision-Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Singapore, 6–10 December 2023; pp. 292–305.

• 88.

  Lewis, P.; Perez, E.; Piktus, A.; et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Adv. Neural Inf. Process. Syst. 2020, 33, 9459–9474.

• 89.

  Liu, F.; Lin, K.; Li, L.; et al. Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning. arXiv 2023, arXiv:2306.14565.

• 90.

  Wei, J.; Wang, X.; Schuurmans, D.; et al. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Adv. Neural Inf. Process. Syst. 2022, 35, 24824–24837.

• 91.

  Mao, J.; Ye, J.; Qian, Y.; et al. A Language Agent for Autonomous Driving. arXiv 2023, arXiv:2311.10813.

• 92.

  Peng, Q.; Chen, X.; Yang, C.; et al. ColaVLA: Leveraging Cognitive Latent Reasoning for Hierarchical Parallel Trajectory Planning in Autonomous Driving. arXiv 2025, arXiv:2512.22939.

• 93.

  Turpin, M.; Michael, J.; Perez, E.; et al. Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting. Adv. Neural Inf. Process. Syst. 2023, 36, 74952–74965.

• 94.

  Ouyang, L.; Wu, J.; Jiang, X.; et al. Training Language Models to Follow Instructions with Human Feedback. Adv. Neural Inf. Process. Syst. 2022, 35, 27730–27744.

• 95.

  Lu, Y.; Tu, J.; Ma, Y.; et al. ReAL-AD: Towards Human-Like Reasoning in End-to-End Autonomous Driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Honolulu, HI, USA, 19–20 October 2025; pp. 27783–27793.

• 96.

  Li, Y.; Javanmardi, E.; Thompson, S.; et al. PrefDrive: Enhancing Autonomous Driving Through Preference-Guided Large Language Models. In Proceedings of the 2025 IEEE Intelligent Vehicles Symposium (IV), Cluj-Napoca, Romania, 22–25 June 2025; pp. 1689–1696.

• 97.

  Fu, H.; Zhang, D.; Zhao, Z.; et al. ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Honolulu, HI, USA, 19–23 October 2025.