3 papers
cs.LG2026
CRAFT: Counterfactual-to-Interactive Reinforcement Fine-Tuning for Driving Policies
Keyu Chen, Nanfei Ye, Yida Wang +4
Open-loop imitation learning has advanced modern autonomous driving policy architectures, but closed-loop deployment remains vulnerable to policy-induced distribution shift. Existi…
cs.CV2025
DriveAgent-R1: Advancing VLM-based Autonomous Driving with Active Perception and Hybrid Thinking
Weicheng Zheng, Xiaofei Mao, Nanfei Ye +4
The advent of Vision-Language Models (VLMs) has significantly advanced end-to-end autonomous driving, demonstrating powerful reasoning abilities for high-level behavior planning ta…
cs.CV2024
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
Lingdong Kong, Shaoyuan Xie, Hanjiang Hu +88
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sen…