Xingyuan Chen

How can I help U?

Personal Introduction

I am a second-year master's student in Control Science and Engineering at the University of Michigan–Shanghai Jiao Tong University Joint Institute, where I work under the supervision of Dr. Hanyang Zhuang at the CyberC3 Intelligent Vehicle Lab, led by Prof. Ming Yang. I received my bachelor’s degree in Artificial Intelligence from Shanghai Jiao Tong University.

My research focuses on multi-vehicle cooperative perception , aiming to develop robust and efficient V2V (Vehicle-to-Vehicle) and V2X (Vehicle-to-Everything) technologies for real-world deployment. I am also deeply interested in control systems for autonomous vehicles and robotics , with a passion for building intelligent, safe, and scalable solutions for future transportation systems.

Research

  • Real-time Real-world Multi-vehicle Collaborative Perception using Fish-eye Surrounding Cameras

    Xingyuan Chen;Yunqi Zhao; Hanyang Zhuang; Chunxiang Wang and Ming Yang

    (Under Review) IEEE Transaction on Intelligent Transportation System(T-ITS), 2025

    [Video]
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  • A Real-time Framework of Multi-Camera Vehicle Tracking System

    Hanyang Zhuang; Xingyuan Chen; Jianglin Lan; Ying Wang; Zhaoxuan Lu; Chunxiang Wang and Ming Yang

    IEEE International Conference on Real-time Computing and Robotics (RCAR), 2024

    [Paper]
    Thumbnail for A Real-time Framework of Multi-Camera Vehicle Tracking System

  • Tactics2D: A Reinforcement Learning Environment Library for Driving Decision making

    Yueyuan Li; Songan Zhang; Mingyang Jiang; Xingyuan Chen; Jing Yang; Yeqiang Qian; Chunxiang Yang and Ming Yan

    IEEE Transactions on Intelligent Vehicles(T-IV),2024

    [Paper] [Code]
    Thumbnail for Tactics2D: A Reinforcement Learning Environment Library for Driving Decision making

Internship

DiDi Company Internship Practice

Research Intern

Time: 2022/06-2022/10

I participated in a project focused on improving motion planning multi-modality for autonomous vehicles. My work involved researching trajectory prediction and applying perturbation-based data augmentation techniques using reinforcement learning methods within the CARLA simulation environment.

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Momenta Company Internship Practice

R&D Intern

Time: 2025/01-2025/04

I contributed to the mining and preprocessing of lane change scenarios aimed at mitigating undesirable lane change behaviors in autonomous driving systems. Specifically, I developed a vision-language model (VLM)-based scenario checker to identify and filter invalid early and late lane change instances within urban intersection datasets.

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Legged Robot Lab Internship Practice

Time: 2025/05-Present

Work for robotic dog navigation and we are still working on it

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