BSci (Traffic Engineering), MSc (Transportation Engineering)
Ying has a research background in connected and automated vehicles and traffic flow theory. His previous work focused on stochastic driving behavior modeling, platoon control strategies, and human-vehicle interaction, contributing to a deeper understanding of mixed traffic dynamics. Currently, he is dedicated to integrating artificial intelligence with traffic flow theory to address complex traffic safety issues in heterogeneous traffic environments. His long-term goal is to bridge the gap between human behavior modelling and automated vehicle control, ensuring a smoother transition toward fully autonomous transportation systems.
DOCTORAL RESEARCH
“Safe Leadership of Autonomous Vehicles over Human-Driven Vehicles Based on Deep Reinforcement Learning: A Risk Assessment and Optimal Control Framework for Mixed Platoons”
THESIS SUPERVISORS
Dr Xiaomeng Li, Professor Ashish Bhaskar, Dr Mohammed Elhenawy, Professor Sebastien Glaser
RESEARCH INTERESTS
- Connected and Automated Vehicles
- Traffic Flow Theory
- Artificial Intelligence
- Driving Behaviour Modelling
AWARDS
- 2025 QUT Postgraduate Research Award (QUTPRA) (International) and QUT HDR Tuition Fee Sponsorship