1 citations · 1 across the 5 of their papers we have counts for
5 papers
ForeDrive: Foresight-Guided End-to-End Autonomous Driving with a Planning-Relevant Latent World Model
Sinuo Wang, Zichong Gu, Yuhan Huang +10
Existing latent world models are typically optimized for future predictability, yet the resulting representations are not necessarily useful for planning in autonomous driving. Pre…
X-Driver: Explainable Autonomous Driving with Vision-Language Models
Wei Liu, Jiyuan Zhang, Binxiong Zheng +3
End-to-end autonomous driving has advanced significantly, offering benefits such as system simplicity and stronger driving performance in both open-loop and closed-loop settings th…
Situation-aware Autonomous Driving Decision Making with Cooperative Perception on Demand
Wei Liu
This paper investigates the impact of cooperative perception on autonomous driving decision making on urban roads. The extended perception range contributed by the cooperative perc…
CIMRL: Combining IMitation and Reinforcement Learning for Safe Autonomous Driving
Jonathan Booher, Khashayar Rohanimanesh, Junhong Xu +5
Modern approaches to autonomous driving rely heavily on learned components trained with large amounts of human driving data via imitation learning. However, these methods require l…
Uniformly Safe RL with Objective Suppression for Multi-Constraint Safety-Critical Applications
Zihan Zhou, Jonathan Booher, Khashayar Rohanimanesh +3
Safe reinforcement learning tasks are a challenging domain despite being very common in the real world. The widely adopted CMDP model constrains the risks in expectation, which mak…