9 citations · 19 across the 11 of their papers we have counts for
15 papers
Physics-Informed Koopman Learning for Data-Efficient System Identification of Automated Vehicle Dynamics
Hangyu Li, Yiming Meng, Zhaohui Liang +8
Understanding how a particular automated vehicle reacts to nearby traffic can improve motion prediction, control development, traffic simulation, and safety evaluation. However, co…
TransCAVE-E: A distributed virtual reality testbed for adaptive external human-machine interfaces
Yun Ye, Zexuan Li, Haoyang Liang +3
External human-machine interfaces (eHMIs) are evolving from predefined displays toward adaptive communication strategies that respond to changing traffic and road-user states. This…
DecoupleGS: Interactive 3D Gaussian Splatting for End-to-End Autonomous Driving Testing
Siying Li, Ying Ni, Jie Sun +2
End-to-end (E2E) autonomous driving algorithms require rigorous closed-loop validation in simulation environments offering high visual fidelity, strong interactivity, and real-time…
External Human-Machine Interface based on Intent Recognition: Framework Design and Experimental Validation
Boya Sun, Haotian Shi, Ying Ni +2
Increasing autonomous vehicles (AVs) in transportation systems makes effective interactions between AVs and pedestrians indispensable. External human--machine interface (eHMI), whi…
Real-Time Lane-Level Crash Detection on Freeways Using Sparse Telematics Data
Shixiao Liang, Chengyuan Ma, Pei Li +7
Real-time traffic crash detection is critical in intelligent transportation systems because traditional crash notifications often suffer delays and lack specific, lane-level locati…
Steerable Adversarial Scenario Generation through Test-Time Preference Alignment
Tong Nie, Yuewen Mei, Yihong Tang +5
Adversarial scenario generation is a cost-effective approach for safety assessment of autonomous driving systems. However, existing methods are often constrained to a single, fixed…
Interaction Dataset of Autonomous Vehicles with Traffic Lights and Signs
Zheng Li, Zhipeng Bao, Haoming Meng +4
This paper presents the development of a comprehensive dataset capturing interactions between Autonomous Vehicles (AVs) and traffic control devices, specifically traffic lights and…
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…
FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction
Junwei You, Rui Gan, Weizhe Tang +9
Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS). Although deep learning-based approaches - especially those…
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…
Bi-scale Car-following Model Calibration for Corridor Based on Trajectory
Keke Long, Haotian Shi, Zhiwei Chen +3
The precise estimation of macroscopic traffic parameters, such as travel time and fuel consumption, is essential for the optimization of traffic management systems. Despite its imp…
A Physics Enhanced Residual Learning (PERL) Framework for Vehicle Trajectory Prediction
Keke Long, Zihao Sheng, Haotian Shi +3
In vehicle trajectory prediction, physics models and data-driven models are two predominant methodologies. However, each approach presents its own set of challenges: physics models…