4 citations · 8 across the 8 of their papers we have counts for
5 papers · 1 filter
Online Adaptive Platoon Control for Connected and Automated Vehicles via Physics Enhanced Residual Learning
Peng Zhang, Heye Huang, Hang Zhou +3
This paper introduces a physics enhanced residual learning (PERL) framework for connected and automated vehicle (CAV) platoon control, addressing the dynamics and unpredictability…
Physics Enhanced Residual Policy Learning (PERPL) for safety cruising in mixed traffic platooning under actuator and communication delay
Keke Long, Haotian Shi, Yang Zhou +1
Linear control models have gained extensive application in vehicle control due to their simplicity, ease of use, and support for stability analysis. However, these models lack adap…
V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models
Junwei You, Haotian Shi, Zhuoyu Jiang +6
Vehicle-to-everything (V2X) cooperation has emerged as a promising paradigm to overcome the perception limitations of classical autonomous driving by leveraging information from bo…
VLM-MPC: Vision Language Foundation Model (VLM)-Guided Model Predictive Controller (MPC) for Autonomous Driving
Keke Long, Haotian Shi, Jiaxi Liu +1
Motivated by the emergent reasoning capabilities of Vision Language Models (VLMs) and their potential to improve the comprehensibility of autonomous driving systems, this paper int…
Online Physical Enhanced Residual Learning for Connected Autonomous Vehicles Platoon Centralized Control
Hang Zhou, Heye Huang, Peng Zhang +3
This paper introduces an online physical enhanced residual learning (PERL) framework for Connected Autonomous Vehicles (CAVs) platoon, aimed at addressing the challenges posed by t…