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WS-12：Connected Autonomous Systems and Applications (CASA) Session Chair: Prof. Jianhua He, University of Essex, UK
Tencent voov meeting link for online participants: https://voovmeeting.com/dm/SIue3TTG8JMh #VooVMeeting No：362-025-175 Time: 2023/08/10 14:00-15:30 (GMT+08:00)
Oral Session: 10th Aug., 14:00-15:00 （China Standard Time） [14:00-14:15] Multi-robot Cooperative Transport Simulation System Xiaodong Li, Yangfei Lin, Zhaoyang Du, Rui Yin and Celimuge Wu (The University of Electro-Communications, Japan) [14:15-14:30] Graph Convolutional Integration based Distributed Multi-View Learning in Urban Air Mobility Kai Xiong, Rui Wang, Supeng Leng, Quanxin Zhao, and Meie Peng (University of Electronic Science and Technology of China, China) [14:30-14:45] Cooperative Vehicle Identification for Safe Cooperative Autonomous Driving Zuoyin Tang (Aston University, UK), Jianhua He (University of Essex, UK) and Jiawei Zheng (Beijing University of Post and Telecommunications, China) [14:45-15:00] A GNSS Positioning Algorithm Assisted by LSTM Neural Network and EKF Jin Wang, Doudou Tang, Shaoqing Lv, Pengwu Wan and Xiyi Dong （Xi’an University of Posts and Telecommunications, China）
Keynote Session: 10th Aug., 15:00-15:30 （China Standard Time） Keynote 1: Prof. Wei Wang, Huazhong University of Science and Technology, China (Online) Title: Mobile Communication/Computing Enhanced Intelligence in Connected and Autonomous Vehicles Time: 15:00-15:15, Thursday, 10 August Abstract: To improve transport efficiency and safety, connected and autonomous vehicles (CAVs) leverage distinct sensing modalities and dynamic networking to break through the perception limitation of a single vehicle. CAV is an integration of two of the most promising automotive technologies, connected vehicles and autonomous vehicles. In CAV local perception from autonomous vehicles is shared with other vehicles and infrastructure to build a comprehensive perception of driving environments and cooperative driving. This talk will present our recent efforts to address several essential challenges in achieving CAV. In particular, this talk will introduce a spatial-temporal adaptive computation offloading solution for video semantic segmentation in CAV to address the bandwidth limitation issue, a mmwave automotive radar metric localization solution to address the Doppler effect issue, and a wireless-assisted vision SLAM solution to extend it to dark and textureless environments. Bio: Wei Wang is currently a full professor with the School of Electronic Information and Communications, Huazhong University of Science and Technology. He received the Ph.D. degree from the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology. His research interests include PHY/MAC design and mobile computing in wireless systems. He has published 2 books and 90 refereed papers in international leading journals and primer conferences. He is the inventor of 3 US and 20 Chinese patents. He won the best paper award in IEEE ICC 2019. Keynote 2: Jiang Zhu, Tsinghua University, China (Online) Tramcar speed optimization system based on intelligent vehicle infrastructure cooperative cloud-speed-control algorithm Time: 15:15-15:30, Thursday, 10 August Abstract: Tramcars are economical, comfortable, energy-saving, and environmentally friendly, and will play an increasingly important role in the future transportation system. In theory, its signal system can be connected to traffic lights to prioritize tramcars. However, in reality, there are still many places where trams do not get priority at intersections, resulting in long waiting times and low efficiency. Some prioritized trams may also occupy traffic resources, causing negative feedback. The project aims to develop a vehicle-road-cloud decision-making system to optimize tram operations, including driving speed and station parking time, to reduce travel time and intersection traffic resources. The study will be based on the actual operation data of Jiaxing Tramcar Line 1 and build a cooperative vehicle infrastructure system. Step-by-step simulation testing, real car testing, trial operation, and practical application are planned. This project has high application value and scalability. Bio: Jiang Zhu is a technology researcher in Yangtze Delta Region Institute of Tsinghua University, Zhejiang, specializing in the research and application of cloud control technology. He has experience in autonomous driving, vehicle perception and decision-making, intelligent connected vehicle testing, and vehicle-road cooperation technology. He led the BRT speed cloud-control system, achieving fully adaptive driving with speed control for 2 years. He also participated in the project "2021 Industrial Technology Basic Public Service Platform - Construction of Intelligent Connected Vehicle Control Basic Platform" responsible for the development of collaborative decision-making planning and control module for connected autonomous driving scenarios. |
