Learning-Based Control of Multiple Connected Vehicles in the Mixed Traffic by Adaptive Dynamic Programming

The emergence of connected and autonomous vehicles (CAVs) has increased opportunities to mitigate the traffic congestion, improve safety and reduce accidents. In this paper, we consider the mixed traffic case with multiple heterogeneous human-driven vehicles and multiple CAVs on freeways. Under mild conditions, the stabilizability of the overall system is proved. With the tracking errors of relative distance and velocity as the states, we design an input-to-state stabilizing controller that solves a linear quadratic regulator problem by means of reinforcement learning and adaptive dynamic programming techniques. The priori knowledge of the vehicle network model is not needed. For a string of connected human-driven and automated vehicles, we give the sufficient conditions to guarantee the general string stability. The proposed learning-based control methodology is validated by means of simulation results.