arXiv:2608. 07621v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently achieved impressive performance for end-to-end autonomous driving, yet existing approaches are primarily designed for an individual single autonomous driving agent with limited support for cooperative perception, reasoning, and planning.
By Hsu-kuang Chiu, Stephen F. Smith
arXiv:2505. 18334v2 Announce Type: replace-cross Abstract: Past work has demonstrated that autonomous vehicles can drive more safely if they communicate with each other.
By Jiaxun Cui, Chen Tang, Jarrett Holtz, Janice Nguyen, Alessandro G. Allievi, Hang Qiu, Peter Stone
The paper introduces the KITScenes LongTail dataset, a curated collection of rare driving scenarios designed to evaluate how well reasoning models in autonomous driving follow their own reasoning. The authors find that many current models frequently diverge between the actions they state in their reasoning chains and the actions they actually execute, a phenomenon they term incoherence. They show that when the reasoning and execution disagree, the reasoning is often correct, and enforcing coherence via a kinematic model can improve motion planning, indicating that coherent action based on stated reasoning is essential for trustworthy autonomous driving.
By Royden Wagner, Omer Sahin Tas, Jaime Villa, Felix Hauser, Yinzhe Shen, Marlon Steiner, Dominik Strutz, Carlos Fernandez, Quentin Delfosse, Christoph Weinhuber, Christian Kinzig, Guillermo S. Gutierrez-Cabello, Hendrik K\"onigshof, Fabian Immel, Richard Schwarzkopf, Nils Alexander Rack, Kevin R\"osch, Kaiwen Wang, Jan-Hendrik Pauls, Martin Lauer, Igor Gilitschenski, Holger Caesar, Christoph Stiller
arXiv:2609.15169v1 Announce Type: new
Abstract: Driving vision-language-action (VLA) models increasingly reason before acting, but their intermediate reasoning is often weakly grounded in physical sc...
By Xiao Liu, Haoyu Li, Jianghao Leng, Lin Wang, Chao Sun
arXiv:2609. 28366v1 Announce Type: cross Abstract: Vision-language models (VLMs) offer a promising approach to long-tail autonomous driving, but existing driving datasets provide limited supervision for connecting decision-critical visual evidence with reasoning and planning.
By Zhipeng Bao, Wenjie Zhao, Tianle Zhu, Haohua Que, Chence Yang, Geng Yuan, Qianwen Li
arXiv:2608. 20129v1 Announce Type: cross Abstract: Autonomous vehicles require robust perception and decision-making capabilities to operate in diverse and unseen scenarios.
By Mehdi Azarafza, Faezeh Pasandideh, Ali Ehteshami Bejnordi, Stefan Henkler, Achim Rettberg