Roadmap to Cooperative & Automated Transportation

The world has placed high hopes in automated vehicle (AV) technologies in revolutionizing transportation system performance, including multiplying roadway capacity and minimizing energy consumption. However, research conducted by Dr. Xiaopeng (Shaw) Li and colleagues has found that existing production AVs exhibit comparable or even inferior performance compared to human-driven vehicles (HDV). To bridge this gap and realize the full potential of AVs, Dr. Li will propose a roadmap of cooperative & automated transportation, from optimal trajectory control in ideal conditions through a cooperative control framework incorporating edge computing and machine learning under real-world constraints. This analysis of ideal conditions (e.g., pure AV with perfect information and control) reveals critical theoretical properties specifying feasible time-space ranges of AV 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. The solutions discussed by Dr. Li will serve as the building blocks for solving more realistic AV control problems (e.g., traffic mixed with human drivers, considering different cooperation classes, with stochasticity and errors).