arXiv AI

VISTA-DZ: Visual Semantic Trajectory Adaptation for Personalized Dilemma Zone Prediction

arXiv:2606. 29548v1 Announce Type: cross Abstract: Driver decision making in the dilemma zone at signalized intersections is safety critical, as vehicles approaching a yellow signal must decide whether to stop or proceed within limited time and distance margins.

arXiv AI

Jun 30

LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving

arXiv:2606. 29879v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) provide powerful semantic understanding and commonsense reasoning for End-to-End Autonomous Driving (E2E-AD) planning.

By Chen Yang, Yuhao Wei, Ze Xu, Ziheng Zou, Shuang Liang, Delin Ouyang, Lingfeng Qi, Jie Li, Guofa Li

llms agents robotics multimodal benchmarks

arXiv AI

Jul 7

AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning

arXiv:2607. 03182v1 Announce Type: cross Abstract: Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories.

By Qi Liu, Yabei Li, Hongsong Wang, Heng Zhang, Lei He

llms agents robotics multimodal benchmarks safety

arXiv AI

Jun 17

DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models

arXiv:2606. 17362v1 Announce Type: cross Abstract: Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-dependent.

By Xinglong Sun, Kevin Xie, Jenny Schmalfuss, Despoina Paschalidou, Xiuming Zhang, Sanja Fidler, Kashyap Chitta, Jose M. Alvarez

llms agents robotics multimodal benchmarks

arXiv AI

Jun 24

UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving

arXiv:2606. 24759v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade-off between temporal reasoning and spatial precision.

By Xiaowei Gao, Pengxiang Li, Yitai Cheng, Ruihan Xu, James Haworth, Stephen Law, Yun Ye

llms agents robotics multimodal benchmarks safety

arXiv AI

Aug 3

Auto-JEPA: A Latent World Model of Continuous Intent for End-to-End Autonomous Driving

arXiv:2607. 29031v1 Announce Type: cross Abstract: Existing autonomous-driving world models typically perform dense prediction of future videos, occupancy states, BEV representations, or agent motion.

By Jiwei Yang, Zhengxian Chen, Chaosheng Huang, Jun Li

rag agents robotics

arXiv Machine Learning

Jul 28

DriveDNA: A Large-Scale Multimodal Naturalistic Driving Dataset and Benchmark for Driving Style Identification

arXiv:2607. 23822v1 Announce Type: new Abstract: Driving style captures stable, driver-specific patterns in how a vehicle is driven.

By Yuhang Wang, Lingyao Li, Hao Zhou

multimodal benchmarks

arXiv Machine Learning

Jul 9

A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving

arXiv:2607. 07103v1 Announce Type: new Abstract: Safe autonomous driving requires both rapid responses to common high-risk events and deeper reasoning over rare, extreme long-tail scenarios in traffic safety.

By Heye Huang, Jingguang Li, Zhiyuan Zhou, Paul Liang, Mingyu Wu, Kitae Jang, Jianqiang Wang

llms agents robotics safety

arXiv AI

Jun 16

OmniTraffic: A Controllable Generation Pipeline and Benchmark for Spatio-Temporal Traffic Reasoning

arXiv:2606. 15749v1 Announce Type: cross Abstract: Traffic scene understanding requires models to reason beyond object recognition, including lane topology, multi-view geometry, temporal evolution, and signal-phase semantics.

By Maonan Wang, Zhengyan Huang, Kemou Jiang, Yuhang Fu, Jiayue Zhu, Yuxin Cai, Xingchen Zou, Qiaosheng Zhang, Yi Yu, Ding Wang, Xi Chen, Ben M. Chen, Yuxuan Liang, Zhiyong Cui, Man On Pun, Yirong Chen

fine-tuning multimodal benchmarks

arXiv AI

Jul 2

What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models

arXiv:2607. 00283v1 Announce Type: cross Abstract: Autonomous vehicles must safely navigate complex environments where planning-critical agents may be hidden from view.

By Amirhosein Chahe, Tyler Naes, Jovin D'sa, Faizan M. Tariq, Sangjae Bae, Lifeng Zhou, David Isele

llms agents robotics fine-tuning multimodal benchmarks

arXiv AI

1d ago

NARRATE: A Multimodal Real-World Australian Driving Dataset for Human-Centred Explanations in Automated Driving

arXiv:2608. 14767v1 Announce Type: cross Abstract: Automated vehicles must explain their decisions in ways that passengers can understand, monitor, and trust.

By Ashkan Yousefi Zadeh, Zishuo Zhu, Xiaomeng Li, Andry Rakotonirainy, Sebastien Glaser, Ronald Schroeter, Patricia Delhomme, Zahra Mehraban

multimodal benchmarks

arXiv AI

Jun 26

Risk-Aware Selective Multimodal Driver Monitoring with Driver-State World Modeling

arXiv:2606. 26922v1 Announce Type: cross Abstract: Continuous driver monitoring in automated vehicles requires low-latency inference while avoiding unsafe decisions under uncertain driver states.

By Daosheng Qiu, Haozhuang Chi, Hao Su, Shu Long, Xinyue Miao, Yongle Dong, Wei Zhang

llms efficiency multimodal safety

arXiv AI

Jul 29

Real-Time Driver Safety Scoring Through Inverse Crash Probability Modeling

arXiv:2603. 14841v3 Announce Type: replace-cross Abstract: Road crashes remain a leading cause of preventable fatalities.

By Joyjit Roy, Samaresh Kumar Singh, Sushanta Das

agents safety

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