9 papers
DriveVer: Lightweight Trajectory Evaluator as Test-Time Verifier for Autonomous Driving
Chong He, Yuechen Luo, Fang Li +2
End-to-end autonomous driving models often encounter performance bottlenecks, as training-time scaling leads to high computational costs and diminishing marginal returns. Existing…
DriveReward: A Comprehensive Dataset and Generative Vision-Language Reward Model for Autonomous Driving
Qimao Chen, Fang Li, Yuechen Luo +11
Reward models play a pivotal role in reinforcement learning (RL) and multi-modal trajectory selection for autonomous driving. However, acquiring such rewards typically relies on ha…
Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation
Jinghui Lu, Jiayi Guan, Zhijian Huang +47
Chain-of-Thought (CoT) reasoning has become a powerful driver of trajectory prediction in VLA-based autonomous driving, yet its autoregressive nature imposes a latency cost that is…
LaST-VLA: Thinking in Latent Spatio-Temporal Space for Vision-Language-Action in Autonomous Driving
Yuechen Luo, Fang Li, Shaoqing Xu +10
While Vision-Language-Action (VLA) models have revolutionized autonomous driving by unifying perception and planning, their reliance on explicit textual Chain-of-Thought (CoT) lead…
Unleashing VLA Potentials in Autonomous Driving via Explicit Learning from Failures
Yuechen Luo, Qimao Chen, Fang Li +5
Vision-Language-Action (VLA) models for autonomous driving often hit a performance plateau during Reinforcement Learning (RL) optimization. This stagnation arises from exploration…
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 +11
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…