Physics-Informed Learning and Control of Connected and Autonomous Vehicles for Congestion Reduction

Building upon previous work in lane changing using physics-informed machine learning for autonomous vehicles, the goal of this project is to develop physics-informed machine learning and data-driven control-based tools for the combined longitudinal and lateral planning and control of connected and autonomous vehicles (CAVs). This research initiative holds the promise to have the following advantages: 1) Utilizing physics-informed machine learning as a tool can significantly enhance computational efficiency, which is beneficial for real-time control in complex scenarios; 2) Combining neural networks with physical models can greatly reduce over-reliance on data; 3) During the training phase of neural networks, any differentiable objective function and various constraints can be considered, allowing it to solve constrained multi-objective model predictive control problems without affecting computational speed. In addition, this project will design a lane-change decision-making module based on deep reinforcement learning and validate the congestion-reducing scheme using NGSIM data and SUMO simulations.