Short Description

IEEE-ITSS logo with a China flag.

The IEEE Intelligent Transportation Systems Society (ITSS) Beijing Chapter is dedicated to advancing the field of intelligent transportation systems through research, education, and collaboration. It aims to promote the development and application of intelligent transportation technologies for the betterment of the cites, fostering innovation and improving the efficiency, safety, and sustainability of transportation systems in the region.

Chair: Yisheng Lv
Secretary: Hongxia Zhao

Chapter Activities

The ITSS Beijing Chapter undertakes various activities to achieve its goals in advancing intelligent transportation systems for the betterment of the cities. Here is an outline of some of the key steps and activities taken by the chapter:

  • ITS Research: Organizing seminars, and webinars and inviting renowned experts and practitioners to deliver keynote speeches and present cutting-edge research findings.
  • ITS Education: Organizing workshops aimed at introducing participants to the world of Intelligent Transportation Systems, which may increase access to education and career opportunities to the students who have an interest in autonomous driving, smart mobility, or ITS.
  • ITS Collaboration: Co-organizing workshops, conferences, and symposiums with relevant universities and research institutes in the ITS field, e.g. the Chinese Association of Automation, the China Intelligent Transportation Systems Association, to foster knowledge sharing and networking opportunities.


1. Keynote Speech: Measurement, Assessment and Modelling of Heavy Goods Vehicle Energy Consumption
Speaker: Dr. Xiaoxiang Na, University of Cambridge
Chair: Prof. Yisheng Lv
Time: 10:00-11:00, July 17, 2023
Venue: No.3 meeting room, Intelligent Building, Institute of Automation, Chinese Academy of Sciences

Abstract: Fuel consumption and carbon emissions of heavy goods vehicles (HGVs) can be reduced by engineering and logistics measures. Engineering measures include vehicle efficiency improvements and changes to energy sources and drivetrains. Both are needed to achieve a nearly carbon emissions target by 2050. Essential approaches in the decarbonisation task are assessment of vehicle performance and computer modelling of vehicle energy consumption. Extensive in-service measurements are needed to provide data for performance assessment and identify parameters for model validation. This presentation will describe recent work on measurement, assessment and modelling of HGV energy consumption, carried out by the Centre for Sustainable Road Freight (SRF) in the UK.

Biography: Dr. Xiaoxiang Na is a University Assistant Professor in Applied Mechanics at the Department of Engineering, University of Cambridge (CUED). He is a lead investigator with the Centre for Sustainable Road Freight (CSRF). His research interests include condition monitoring of road freight vehicles and buses, vehicle energy performance evaluation, and driver-vehicle dynamics. He serves as Associate Editors for IEEE/CAA Journal of Automatica Sinica and IEEE Transactions on Intelligent Vehicles, and co-chairs the Technical Committee on Human-Machine Interface for Connected and Autonomous Vehicles in the IEEE Systems, Man, and Cybernetics Society. He was awarded a Borysiewicz Interdisciplinary Fellowship in December 2021.

2. Keynote Speech: Cooperative Perception “All at Once” with OpenCDA
Speaker: Dr. Jiaqi Ma, University of California, Los Angeles
Chair: Prof. Yisheng Lv
Time: 14:30-15:30, July 31, 2023
Venue: No.2 meeting room, Intelligent Building, Institute of Automation, Chinese Academy of Sciences
VooV Meeting: 869-369-373

Abstract: In this presentation, the latest advancements in vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) cooperative perception aimed at enhancing the safety and efficiency of automated transportation systems will be explored. The presentation will commence with an overview of OpenCDA, an open-source ecosystem tailored to meet the diverse requirements of various communities engaged in cooperative driving automation (CDA) research. This ecosystem establishes a bridge, ensuring seamless integration of development and testing pipelines, and having fostered a wealth of cooperative perception research in the field. Subsequently, four state-of-the-art algorithms developed for V2V and V2X cooperative perception will be introduced. The OP V2V paper offers an open benchmark dataset and fusion pipeline for perception utilizing V2V communication, while V2X-ViT presents a robust cooperative perception framework with V2X communication employing an innovative vision transformer. V2XP-ASG generates adversarial scenes for V2X perception, and HM-ViT introduces a unified multi-agent hetero-modal cooperative perception framework. Lastly, the presentation will feature V2V 4Real, which supplies an extensive real-world dataset and benchmark for V2V cooperative perception, alongside ongoing V2X real-world datasets and experiments taking place at the UCLA smart intersection.

Biography: Dr. Jiaqi Ma is an associate professor at the UCLA Samueli School of Engineering and associate director of UCLA Institute of Transportation Studies. He has led and managed many research projects funded by U.S. DOT, NSF state DOTs, and other federal/state/local programs covering areas of smart transportation systems, such as vehicle-highway automation, intelligent transportation systems (ITS), connected vehicles, shared mobility and large-scale smart system modeling and simulation, and artificial intelligence and advanced computing applications in transportation. He is editor in chief of the IEEE Open Journal of Intelligent Transportation System.

3. Workshop and Special Sessions in ITSC 2023
Date: September 24-28, 2023
Venue: Bilbao, Spain

(1) Combined Workshop on ‘Transportation 5.0’ and ‘FIST: AGI and Transportation Intelligence’

Organizers: Yisheng Lv, Azim Eskandarian, Fei-Yue Wang and Ljubo Vlacic
Contact: Yilun Lin

Four papers were accepted and presented:

  • “Towards Integrated Traffic Control with Operating Decentralized Autonomous Organization” by Yao, Shengyue; Yu, Jingru; Yu, Yi; Xu, Jia; Dai, Xingyuan; Li, Honghai; Wang, Fei-Yue; Lin, Yilun
  • “Building Transportation Foundation Model via Generative Graph Transformer” by Wang, Xuhong; Wang, Ding; Chen, Liang; Wang, Fei-Yue; Lin, Yilun
  • “TransWorldNG: Traffic Simulation via Foundation Model” by Wang, Ding; Wang, Xuhong; Chen, Liang; Yao, Shengyue; Jing, Ming; Li, Honghai; Bao, Shiqiang; Li, Li; Wang, Fei-Yue; Lin, Yilun
  • “Parallel System Based Predictive Control for Traffic Signals in Large-Scale Road Networks” by Dai, Xingyuan; Zhang, Yan; Tang, Yiqing; Chen, Hongrui; Sun, Jiaming; Cong, Fei; Lu, Yanan; Lv, Yisheng

(2) SS29: Decision Recommendation and Decision Intelligence for ITS

Organizers: Peijun Ye, Rui Qin, Juanjuan Li and Junchen Jin
Contact: Hongxia Zhao

Six papers were accepted and presented:

  • “A Framework for Risk-Aware Routing of Connected Vehicles via Artificial Intelligence” by Cardellini, Matteo; Dodaro, Carmine; Maratea, Marco; Vallati, Mauro*
  • “TAO-Based Data Governance in Intelligent Transportation Systems” by liang, xiaolong; Li, Juanjuan; Qin, Rui; Wang, Fei-Yue*
  • “RoW-based Parallel Control for Mix Traffic Scenario: A case study on Lane-Changing” by Yu, Jingru; Yu, Yi; Yao, Shengyue; Wang, Ding; Cai, Pinlong; Li, Honghai; Li, Li; Wang, Fei-Yue; Lin, Yilun*
  • “Parallel Learning Based Foundation Model for Networked Traffic Signal Control” by Zhao, Chen; Dai, Xingyuan; Chen, Yuanyuan; Lin, Yilun; Lv, Yisheng; Wang, Fei-Yue*
  • “Hi-Lane: Hierarchical Decision Support Framework for Traffic Signal Control with Dynamic Lanes” by Ji, Qingyuan; Zhu, Yongdong; Wen, Xiaoyue; Zhang, Qi; Qin, Yuanqi; Jin, Junchen*
  • “DDPG-based Energy-Efficient Train Speed Trajectory Optimization under Virtual Coupling” by Liu, Xuan; Zhou, Min*; Ligang, Tan; Dong, Hairong

(3) SS30: Intelligent Sensing for ITS

Organizers: Xingyuan Dai, Xuan Li, Yongling Tian and Bin Chen
Contact: Yu Shen

Twelve papers were accepted and presented:

  • “Doppler-aware Odometry from FMCW Scanning Radar” by Rennie, Fraser; Williams, David; Newman, Paul; De Martini, Daniele*
  • “Point Cloud Completion by Larger Receptive Fields for Intelligent Vehicles” by Zhang, Yunqing; liu, kunhua; Zhang, Xiaotong; Lv, Yisheng; He, Wei; Chen, Long*
  • “V2I-BEVF: Multi-modal Fusion Based on BEV Representation for Vehicle-to-Infrastructure Perception” by Xiang, Chao; Xie, Xiaopo*; Feng, Chen; bai, zhen; Niu, Zhendong; Yang, Mingchuan
  • “Compression Using Feature Score and Refinement for Deep Neural Networks” by Zhu, Fenghua*; Tian, Bin; Ye, Peijun; Xiong, Gang
  • “Shallow Feature Enhanced Regression Model for UAV Traffic Object Detection” by Li, Mian; Xiong, Gang; Ye, Peijun; Guangmin, Liu; Zhu, Fenghua*
  • “Point Cloud Segmentation for Unstructured Region in Surface Mine” by Huang, Chongqing; Song, Ruiqi; Cui, Chenglin; Li, Xinqing; Ai, Yunfeng*
  • “BEVMOT: A multi-object detection and tracking method in Bird’s-Eye-View via Spatiotemporal Transformers” by Cui, Chenglin; Song, Ruiqi; Li, Xinqing; Ai, Yunfeng*
  • “MSIT-Det: Multi-Scale Feature Aggregation with Iterative Transformer Networks for 3D Object Detection” by Li, Xi; Chen, Yuanyuan; Lv, Yisheng*
  • “VCrash: A Closed-Loop Traffic Crash Augmentation with Pose Controllable Pedestrian” by Lu, Bo; Miao, Qinghai; Dai, Xingyuan; Lv, Yisheng*
  • “Interaction-Aware Trajectory Prediction with Point Transformer” by Liu, Yahui; Dai, Xingyuan; Fang, Jianwu; Lv, Yisheng*
  • “Data Augmentation for Pedestrians in Corner Case: A Pose Distribution Control Approach” by Nie, Yunhao; Chen, Yuanyuan; Miao, Qinghai*; Lv, Yisheng
  • “An MPC-based cooperative control approach for separated power electric multiple units” by Zhang, Zixuan; Cao, Yuan; Su, Shuai; Wang, Di