19 citations · 31 across the 14 of their papers we have counts for
16 papers
How corner is a corner case? Percentile control for highway scenario generation
Jiaxi Liu, Hang Zhou, Hangyu Li +5
Generating corner-case scenarios with appropriate adversity in a simulation environment is critical for testing an autonomous vehicle (AV) software stack's safety performance befor…
Robustness and Resilience Evaluation of Eco-Driving Strategies at Signalized Intersections
Zhaohui Liang, Chengyuan Ma, Keke Long +1
Eco-driving strategies have demonstrated substantial potential for improving energy efficiency and reducing emissions, especially at signalized intersections. However, evaluations…
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…
CATS-V2V: A Real-World Vehicle-to-Vehicle Cooperative Perception Dataset with Complex Adverse Traffic Scenarios
Hangyu Li, Bofeng Cao, Zhaohui Liang +16
Vehicle-to-Vehicle (V2V) cooperative perception has great potential to enhance autonomous driving performance by overcoming perception limitations in complex adverse traffic scenar…
A Low-Rank Method for Vision Language Model Hallucination Mitigation in Autonomous Driving
Keke Long, Jiacheng Guo, Tianyun Zhang +2
Vision Language Models (VLMs) are increasingly used in autonomous driving to help understand traffic scenes, but they sometimes produce hallucinations, which are false details not…
Theory Foundation of Physics-Enhanced Residual Learning
Shixiao Liang, Wang Chen, Keke Long +3
Intensive studies have been conducted in recent years to integrate neural networks with physics models to balance model accuracy and interpretability. One recently proposed approac…
Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen
Zihao Li, Xinyuan Cao, Xiangbo Gao +12
Traffic safety science has long been hindered by a fundamental data paradox: the crashes we most wish to prevent are precisely those events we rarely observe. Existing crash-freque…
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…
Modular Vehicle Routing Problem: Applications in Logistics
Hang Zhou, Yang Li, Chengyuan Ma +2
Recent studies and industry advancements indicate that modular vehicles (MVs) have the potential to enhance transportation systems through their ability to dock and split during a…
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…
Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Prediction
Junwei You, Haotian Shi, Keshu Wu +4
Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS), enhancing road safety and traffic efficiency. While traditi…
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…