Head Image

Zhengwei Bai  


Ph.D. Student, in Electrical and Computer Engineering, University of California, Riverside
Transportation System Research (TSR) Group at Center for Environmental Research and Technology

Education:
M.S., Electrical Information and Engineering, Beijing Jiaotong University
B.E., Electrical Information and Engineering, Beijing Jiaotong University


I am currently a 2nd-year Ph.D. student in Electrical and Computer Engineering at UC, Riverside, co-advised by Dr. Matthew J. Barth and Dr. Guoyuan Wu. Before I started my Ph.D. study at UCR, I received my Master's and Bachalor's degrees under the supervision of Dr. Baigen Cai and Dr. Wei Shangguan at Beijing Jiaotong University.

My current research mainly focus on computer vision for enabling autonomous driving, e.g., 3D object detection, cooperative perception, multi-object tracking, etc. Below are the strengths of mine:

  • Deep Learning based 3D object detection, tracking and reconstruction.
  • Multi-node multi-sensor cooperative perception based on roadside/onboard 3D-LiDAR/Camera.
  • CARLA-based simulation development and data acquisition.
  • Office Address:
    1084 Columbia Avenue
    Riverside, CA 92507
    Email:
    zbai012@ucr.edu

    Find me at:
    ResearchGate
    GoogleScholar
    YouTube

    Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey

    Zhengwei Bai , Guoyuan Wu, Xuewei Qi, Yongkang Liu, Kentaro Oguchi, Matthew J. Barth
    arXiv preprint arXiv:2201.11871
    [Paper], [CITE]

    Cyber Mobility Mirror for Enabling Cooperative Driving Automation: A Co-Simulation Platform

    Zhengwei Bai , Guoyuan Wu, Xuewei Qi, Kentaro Oguchi, Matthew J. Barth
    101st Annual Meeting for Transportation Research Board (TRB2022)
    [Paper], [CITE]

    Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections

    Zhengwei Bai , Peng Hao, Wei Shangguan, Baigen Cai, Matthew J. Barth
    System Architecture, Simulator View, Intersection View,
    Accepted by IEEE Transactions on Intelligent Transportation Systems , 2022, doi: 10.1109/TITS.2022.3145798.
    [Results Video with introduction on YouTube.]

    Deep Reinforcement Learning Based High-level Driving Behavior Decision-making under Heterougeneous Traffic

    Zhengwei Bai , Baigen Cai, Wei Shangguan, Linguo Chai
    [Paper Link], [PDF], [CITE]
    Accepted by Chinese Control Conference 2019
    arXiv:1902.05772v2 [cs.LG]
    [Results Video]

    Deep Learning Based Motion Planning For Autonomous Vehicle Using Spatiotemporal LSTM Network

    Zhengwei Bai , Baigen Cai, Wei Shangguan, Linguo Chai
    [Paper Link], [PDF], [CITE]
    2018 Chinese Automation Congress (CAC), Xi'an, China. , doi: 10.1109/CAC.2018.8623233
    Oral presentation on the session of Unmanned Control System, [PPT]
    [Results Video]

    Design and Implementation of Novel Wireless Field Strength Test System

    Zhengwei Bai
    [Project Introduction]
    Undergraduate Thesis

    Siemens_ITIMS

    Cyber Mobility Mirror (CMM)

    Advisors: Dr. Guoyuan Wu, Dr. Xuewei Qi, Dr. Yongkang Liu, Dr. Kentaro Oguchi, Dr. Wei Matthew Barth

    We focus on building a 3D reconstruction mirror in cyber world based on the perception information from traffic mobilities to support cooperative driving automation (CDA). My research mainly focus on object detection and cooperative perception, which are briefly introduced as follows:

    • To propose 3D object detection, classification, tracking and reconstruction methods.
    • To build up a real-time field operational system to perceive the traffic objects and distribute and display the reconstruction information for enabling CDA.
    • To develop different simulation paltform to support various kinds of perception requirements and model training.


    Siemens_ITIMS

    Reinforcement Learning Based Eco-Approach and Departure (EAD) System.

    Advisors: Dr. Peng Hao, Dr. Guoyuan Wu, Prof. Wei Matthew Barth

    My focus is to propose a RL-based eco-driving strategy under intersections with mixed traffic. My main contributions are shown as follows:

    • Propose a reinforcement learning framework that can deal with logically complex driving task, such as passing a signalized intersection with mixed traffic.
    • Design and Develop a Unity-based reinforcement learning simulator: signalized intersection scenario with CAVs and human-driven vehicles.
    • Design a time-efficient and eco-friendly driving strategy for CAVs by combining vision data, on-board sensor data and V2I communication.


    Siemens_ITIMS

    Intelligent Transportation Information Management System

    Advisors: Prof. Baigen Cai, Prof. Wei Shangguan, Dr. Dakai Yang

    A large-scale transportation information management project I worked on in 2018. My focus is to design and develop several software modules including applications and APIs. My main contributions are shown as follows:

    • Designed and Developed Five Software Modules of the ITIMS including bus, taxi, intersection, traffic signal,s and flow detection modules
    • Developed several high-reliable multi-source data API (Application Programming Interface) which connects to major transportation information database such as The Traffic Police Database in Zhuhai.
    • Management and maintenance overall service infrastructure utilizing remote controller(Using Teamviewer, Xshell and Xftp), Oracle & Mysql database.


    BJTU ITS Lab. ML

    Machine Learning Based Autonomous Vehicle Control Methods

    Advisors: Prof. Baigen Cai, Prof. Wei Shangguan, Dr. Linguo Chai

    A large-scale heterougeneous traffic simulation project I am currently working since 2018. My focus is to explore robust and accurate control methods for CAV in heterougeneous traffic situations. My main contributions are shown as follows:

    • Proposed a deep neural network called spatiotemporal LSTM to generalize the steering angle output by fitting in the raw image data.
    • Proposed a deep Reinforcement Learning (deep RL) based high-level driving behavior decision-making algorithm.
    • Developed a simulation environment based on the Unity3D Engine for the training and testing processes of the deep RL algorithm.


    GNSS

    High-speed Railway Based BeiDou Fusion Positioning Performance Test

    Advisors: Prof. Baigen Cai, Prof. Debiao Lu, Prof. Wei Jiang

    A testing about the performance of the GPS, BeiDou and IMU fusion positioning under high-speed motion scenario (350km/h). My main contributions are shown as follows:

    • Setup the test environment on the train and using device SPAN, UB380 recorded IMU and GPS/BeiDou information separately.
    • Designed the automatic driving test scenarios based on site characteristics (such as the car-flowing scenario, overtaking scenario, obstacles recognition, etc).
    • Wrote the most part (over 80%) of the whole proposal (20134 words in total) and made a presentation to the local transportation department.


    China Railway Urumqi Railway

    China Railway Urumqi Railway Administration Group Co.,Ltd – Field Strength Test system

    Advisors: Prof. Baigen Cai, Prof. Wei Shangguan

    A Field Strength Test System project that I worked on my last year of Undergraduate (2017). The main purpose of the project is to design and develope a system to test, record, and analyze the wireless field strength along the railway. My main contributions are shown as follows:

    • Design and developed a MFC framwork based windows software (about 15000 lines of C++ code) for the novel wireless field strength test (FST) system .
    • Developed and tested the hardware system (collecting and packaging the sensor data such as ODO, GPS and TAX) of the FST system.
    • Testing the whole FST system between Urumqi railway station and Akesu railway station (2018km in total).


    ASCE T&DI Artificial Intelligence Student Competition, 2021

    Honorable Mention

    Cyber Mobility Mirror: Roadside LiDAR-based Real-time Object 3D Detection, Tracking and Reconstruction
    Presentation Video

    "Nokia Cup" Innovation Competition Final, 2016

    1st Prize

    Novel CTCS (Chinese Train Control System) simulation system
    More information

    "BJTU Huiguang Cup" Graduate Academic Culture Festival Essay Competition, 2018

    2nd Prize

    Design and Implementation of Novel Wireless Field Strength Test System

    Beijing Electronic Design Competition Final, 2016

    2nd Prize

    Digital circuit and analog circuit design
    Director: Qinglong Ma
    Teammate: Chenggui Gong
    Team Number: B16281

    Deputy Director of the College Youth League Committee, BJTU, Fall 2014 - Spring 2015


    Secretary of the School League general branch , BJTU, Fall 2015 - Spring 2016


    Volunteer for the 18th China International P&E., Beijing, China, Apr. 2015


    Volunteer for the 2014 APEC Youth Program., Beijing, China, Nov. 2014



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