Dr Kai Chu
- Marie Sklodowska-Curie Future Roads Fellow
Connect
Location
- Bio-Inspired Robotics Lab
About
Dr Kai-Fung Chu is a Marie Skłodowska-Curie Fellow in the Department of Engineering, the University of Cambridge. He received the Ph.D. degree in Electrical and Electronic Engineering from The University of Hong Kong, and the M.Sc. (Distinction) and B.Eng. (First Class Honors) degrees in Electronic and Information Engineering from the Hong Kong Polytechnic University. Prior to joining the University of Cambridge, he was a Research Assistant Professor at the Department of Computing, The Hong Kong Polytechnic University, and a research fellow of the School of Aerospace, Transport and Manufacturing, Cranfield University. He also worked in the industry as an engineer for several years. He won several awards including Outstanding Young Alumni Award 2022 of Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Champion of the Graduate Student Paper Competition 2018 of IEEE (HK) Computational Intelligence Chapter, 2nd runners up of HKUEAA Enginnovate 2018 of HKU Engineering Alumni Association, Chiap Hua Cheng’s Foundation Scholarship 2012 of The Hong Kong Polytechnic University. His research interests include artificial intelligence, optimization, autonomous vehicles, and intelligent transportation systems.
Fellowship period: 15 June 2023 – 14 June 2026
Research
Strategic Themes
Predictive Traffic Data Analysis
Forecasting traffic data according to its high temporal-spatial resolution and real-time characteristic using deep learning models.
Multi-agent Traffic Management
Management of large-scale vehicle fleets and road infrastructure for pre-defined objectives using reinforcement learning.
Trustworthy Artificial Intelligence for Intelligent Transportation Systems
Adversarial machine learning against cyber-physical attacks to applications of intelligent transportation systems.
Research Project
Theme: Automation & robotics
Abstract:
Connected autonomous vehicles (CAVs) will be prevalent given the technology maturity and government promotion. Transportation system administrators and constructors should be prepared to leverage the controllability and potential of CAVs when they gradually permeate the roads in the near future. Current studies for CAVs usually consider either the control of a single CAV with human-driven vehicles (HVs), or collectively control among CAVs only. Moreover, their interactions with intelligent road infrastructure are investigated separately, which oversimplifies the heterogeneity among CAVs, HVs, and intelligent road infrastructure in the near future transportation system. Numerous challenges of heterogeneous multi-agent transportation systems, such as their interactions, partial controllability, and implications, are not addressed. This project will study the control and implications of a heterogeneous multi-agent transportation system mixed with CAVs, HVs, dynamic reversible lanes, and intelligent traffic lights. First, with awareness of the high spatial-temporal resolution and real-time characteristics of transportation systems, an efficient heterogeneous data fusion and multi-agent modelling framework will be developed. Second, optimal control policies for the heterogeneous multi-agent transportation system satisfying the safety requirements will be developed. Third, the opportunities and barriers to practical implementation and the implications for society and governance will be analysed. Through this project, we will push forward the technologies and insights of CAVs at dynamic reversible lanes and intelligent intersections and move towards a safer and more efficient system whereby CAVs can be highly leveraged.
Alignment with SDG: Goal 11 (Sustainable cities and communities)
Project TRL: TRL 3 (Experimental proof of concept)
Industry secondment needs: Real-world or simulated experimental platform for multiple autonomous vehicles
Dr Kai-Fung Chu’s research interests lie at the intersection of artificial intelligence (AI), autonomous vehicles, and intelligent transportation systems (ITS). He develops state-of-the-art AI technologies and their potential breakthrough that apply to various ITS components, such as connected autonomous vehicles, traffic control, road infrastructure, vehicle-to-grid, and vehicular communication. The main goals are to contribute to a deeper understanding of the next-generation transportation system and smart city, and to develop novel intelligent transportation system applications which are efficient, safe, resilient, robust, and ethical.