4 papers
VILTA: A VLM-in-the-Loop Adversary for Enhancing Driving Policy Robustness
Qimao Chen, Fang Li, Shaoqing Xu +9
The safe deployment of autonomous driving (AD) systems is fundamentally hindered by the long-tail problem, where rare yet critical driving scenarios are severely underrepresented i…
Seeing before Observable: Potential Risk Reasoning in Autonomous Driving via Vision Language Models
Jiaxin Liu, Xiangyu Yan, Liang Peng +11
Ensuring safety remains a key challenge for autonomous vehicles (AVs), especially in rare and complex scenarios. One critical but understudied aspect is the \textbf{potential risk}…
MTRDrive: Memory-Tool Synergistic Reasoning for Robust Autonomous Driving in Corner Cases
Ziang Luo, Kangan Qian, Jiahua Wang +13
Vision-Language Models(VLMs) have demonstrated significant potential for end-to-end autonomous driving, yet a substantial gap remains between their current capabilities and the rel…
AdaThinkDrive: Adaptive Thinking via Reinforcement Learning for Autonomous Driving
Yuechen Luo, Fang Li, Shaoqing Xu +10
While reasoning technology like Chain of Thought (CoT) has been widely adopted in Vision Language Action (VLA) models, it demonstrates promising capabilities in end to end autonomo…