3 accepted papers
Imitation learning (IL) has been widely adopted for autonomous driving planning because of its data efficiency and stable optimization. Yet IL-based planners often suffer from causal confusion, fitting spurious correlations instead of genuine causal mechanisms, which leads to unreliable planning beh
Vision-Language Models (VLMs) show promise for autonomous driving, yet their struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. To overcome this, we introduce AgentThink , a pioneering unified framewor
Ensuring safe and socially compliant driving is essential for autonomous vehicle planning. However, one of the significant challenges remains the performance bottleneck caused by interaction uncertainty in complex traffic scenarios. Traditional planning algorithms typically account for all traffic p