108 citations · 111 across the 8 of their papers we have counts for
8 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…
EcoFollower: An Environment-Friendly Car Following Model Considering Fuel Consumption
Hui Zhong, Xianda Chen, PakHin Tiu +2
To alleviate energy shortages and environmental impacts caused by transportation, this study introduces EcoFollower, a novel eco-car-following model developed using reinforcement l…
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…
CAV-AHDV-CAV: Mitigating Traffic Oscillations for CAVs through a Novel Car-Following Structure and Reinforcement Learning
Xianda Chen, PakHin Tiu, Yihuai Zhang +2
Connected and Automated Vehicles (CAVs) offer a promising solution to the challenges of mixed traffic with both CAVs and Human-Driven Vehicles (HDVs). A significant hurdle in such…
GenFollower: Enhancing Car-Following Prediction with Large Language Models
Xianda Chen, Mingxing Peng, PakHin Tiu +4
Accurate modeling of car-following behaviors is essential for various applications in traffic management and autonomous driving systems. However, current approaches often suffer fr…
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…
Towards Explainable Traffic Flow Prediction with Large Language Models
Xusen Guo, Qiming Zhang, Junyue Jiang +4
Traffic forecasting is crucial for intelligent transportation systems. It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns…