88 citations · 95 across the 6 of their papers we have counts for
6 papers
From Words to Wheels: Automated Style-Customized Policy Generation for Autonomous Driving
Xu Han, Xianda Chen, Zhenghan Cai +3
Autonomous driving technology has witnessed rapid advancements, with foundation models improving interactivity and user experiences. However, current autonomous vehicles (AVs) face…
Continual Learning for Adaptable Car-Following in Dynamic Traffic Environments
Xianda Chen, PakHin Tiu, Xu Han +4
The continual evolution of autonomous driving technology requires car-following models that can adapt to diverse and dynamic traffic environments. Traditional learning-based models…
EditFollower: Tunable Car Following Models for Customizable Adaptive Cruise Control Systems
Xianda Chen, Xu Han, Meixin Zhu +4
In the realm of driving technologies, fully autonomous vehicles have not been widely adopted yet, making advanced driver assistance systems (ADAS) crucial for enhancing driving exp…
Generating and Evolving Reward Functions for Highway Driving with Large Language Models
Xu Han, Qiannan Yang, Xianda Chen +2
Reinforcement Learning (RL) plays a crucial role in advancing autonomous driving technologies by maximizing reward functions to achieve the optimal policy. However, crafting these…
EnsembleFollower: A Hybrid Car-Following Framework Based On Reinforcement Learning and Hierarchical Planning
Xu Han, Xianda Chen, Meixin Zhu +3
Car-following models have made significant contributions to our understanding of longitudinal driving behavior. However, they often exhibit limited accuracy and flexibility, as the…
FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling
Xianda Chen, Meixin Zhu, Kehua Chen +5
Car-following is a control process in which a following vehicle (FV) adjusts its acceleration to keep a safe distance from the lead vehicle (LV). Recently, there has been a booming…