新框架提升自动驾驶VLM的可靠性

报道来源 [5]

1. arXiv cs.AI TIER_1 English(EN) · Zhaohong Liu, Hao Ye, Xianlin Zhang, Mengshi Qi · 2026-07-07 04:00

   CritiqueDriveVLM: From Verifier-Guided Reinforcement Learning to Latent Thought Distillation for Autonomous Driving

   arXiv:2607.04179v1 Announce Type: cross Abstract: End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving. However, standard Supervised Fine-Tuning (SFT) often suffers from reasoning hallucinations and conservative biases. While traditional tool-augm…

2. arXiv cs.LG TIER_1 English(EN) · Zhuoren Li, Guizhe Jin, Ran Yu, Weiqi Zhang, Zhiwen Chen, Nan Li, Lu Xiong, Ilya Kolmanovsky, Dimitar Filev, Bo Leng, Jia Hu · 2026-07-07 04:00

   A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective

   arXiv:2503.23650v2 Announce Type: replace Abstract: Reinforcement learning (RL), with its ability to explore and optimize policies in complex, dynamic decision-making tasks, has emerged as a promising approach to addressing motion planning (MoP) challenges in autonomous driving (…

3. arXiv cs.CV TIER_1 English(EN) · Yunxiao Shi, Hong Cai, Mohammad Ghavamzadeh, Fatih Porikli · 2026-07-07 04:00

   CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving

   arXiv:2607.02841v1 Announce Type: cross Abstract: End-to-end autonomous driving (E2E-AD) aims to directly map raw sensor information to driving actions. Recently, with the rapid advancement of multi-modal large language models (MLLMs), researchers have proposed the paradigm of Vi…

4. arXiv cs.CV TIER_1 English(EN) · Junru Gu, Lijin Yang, Jianing Huang, Shu Liu, Zhongzhan Huang, Hang Zhao · 2026-07-07 04:00

   Agent-driven Long-tail Simulation for Autonomous Driving

   arXiv:2607.04331v1 Announce Type: cross Abstract: Evaluating autonomous driving systems in closed-loop settings requires realistic and interactive simulation, yet existing simulators largely rely on log replay or rule-based agents, limiting behavioral diversity and long-tail cove…

5. arXiv cs.CV TIER_1 English(EN) · Argho Dey, Yunfei Yin, Swachha Ray, Md Minhazul Islam, Zheng Yuan, Sijing Xiong, Hongyu Liu, Zhiqiu Huang · 2026-07-07 04:00

   A Reliable Context-Aware and Temporal Planning Framework for Autonomous Driving

   arXiv:2607.04689v1 Announce Type: cross Abstract: Safe operation of autonomous vehicles in dense urban traffic depends on perception and planning that remain reliable when onboard sensing is degraded. In real driving conditions, camera observations are frequently corrupted by occ…