arXiv AI August 11, 2026 By Hsu-kuang Chiu, Stephen F. Smith

CMU-Drive and V2V-VLA: Cooperative Multi-agent Unified Driving with Reasoning Benchmark and Vehicle-to-Vehicle Vision-Language-Action Models

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.

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arXiv Computer Vision

4d ago

CoVLM-Bench: A Real-World Benchmark for Cooperative Driving Question Answering and Planning

arXiv:2609.35823v1 Announce Type: new Abstract: Vision-language models (VLMs) have made substantial progress in autonomous driving, but their success has primarily been studied in ego-centric scenes....

By Kang Yang, Shuai Liu, Hang Li, Yance Fang, Deying Li, Yongcai Wang

llms agents nlp robotics multimodal benchmarks

arXiv AI

Jul 28

CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning

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

llms agents robotics

arXiv Machine Learning

Jun 15

RT-VLA: Real-Time Vision-Language-Action Models via Knowledge Distillation

arXiv:2606. 14010v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for end-to-end autonomous driving by jointly modeling visual perception, language reasoning, explainability and action prediction.

By Xiangyu Huang, Zhenlin Hua, Han Zhou, Shounak Sural, Ragunathan Rajkumar

agents robotics efficiency multimodal benchmarks safety

arXiv Computer Vision

Aug 24

A Collaborative Multi-Modality Interaction for VLA-based End-to-End Autonomous Driving

arXiv:2608.20890v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a powerful paradigm for end-to-end autonomous driving by jointly integrating perception, reasoning,...

By Jingtao Sun, Xiaohai He, Yike Zhang, Dong Huang, Yaonan Wang, Ajmal Mian, Mike Zheng Shou

agents nlp robotics multimodal safety

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

4d ago

VLALight: A Vision-Language-Action Model for Traffic Signal Control

arXiv:2609.36934v1 Announce Type: new Abstract: Traffic signal control (TSC) is essential for improving urban mobility and reducing congestion. Although roadside cameras are widely deployed at signal...

By Pan Zhang, Siqi Lai, Kemu Dong, Hao Liu

llms agents reinforcement-learning multimodal benchmarks

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CoVLM-Bench: A Real-World Benchmark for Cooperative Driving Question Answering and Planning

CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning

RT-VLA: Real-Time Vision-Language-Action Models via Knowledge Distillation

A Collaborative Multi-Modality Interaction for VLA-based End-to-End Autonomous Driving

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

VLALight: A Vision-Language-Action Model for Traffic Signal Control