Title

Roadmap to Cooperative & Automated Transportation: Theory, Modeling, and Experiments

Abstract

Scholars have put high hopes on connected & automated vehicle (CAV) technologies in revolutionizing transportation system performance, including multiplying roadway capacity and minimizing energy consumption. However, our study observed that existing production AVs exhibit comparable or even inferior performance compared with human-driven vehicles (HVs). To bridge the discrepancy and realize the full potential of CAVs, we propose a roadmap of cooperative & automated transportation from optimal trajectory control in the ideal conditions through a cooperative control framework incorporating edge computing and machine learning under real-world constraints. The analysis of ideal conditions (e.g., pure CAV with perfect information and control) reveals critical theoretical properties specifying feasible time-space ranges of CAV movements. Combined with customized mathematical programming and control methods, these properties lead to efficient solutions (e.g., in milliseconds) to real-time optimal trajectory planning problems. These solutions serve as the building blocks for solving more realistic CAV control problems (e.g., mixed with human drivers, considering different cooperation classes, with stochasticity and errors). Further, significant efforts have been made in developing hardware technologies and physical experiments with both reduced-scale and full-scale CAV test facilities to transfer modeling results to physical applications and implementable systems.

Presentation

Dr. Li's Presentation

Speaker Bio

Dr. Xiaopeng (Shaw) Li is currently the Vasant Surti associate professor in the Department of Civil and Environmental Engineering with a courtesy appointment in the Department of Computer Science and Engineering at the University of South Florida (USF). He is the director for one USDOT national university transportation center, National Institute for Congestion Reduction (NICR). He established the Connected and Automated Transportation Systems Lab that developed a multi-scale CAV testbed including multiple full-scale and reduced-scale CAVs and associated roadside units. He is the first holder of the Susan A. Bracken Faculty Fellowship at USF prior to the Vasant Surti Fellowship. He is a recipient of a National Science Foundation (NSF) CAREER award. He has served as the PI or a co-PI for a number of federal (NSF, USDOT, USDOE), local (e.g., state DOTs, UTCs, I-4 Corridor Program), and industry grants. He has published over 90 peer-reviewed journal papers. His major research interests include automated vehicle traffic control and connected & interdependent infrastructure systems. Please check http://cee.eng.usf.edu/faculty/xiaopengli/index.html for more information.

Speaker Headshot

Xiaopeng Li.jpeg