Scenario-based Hybrid Model Predictive Design for Cooperative Adaptive Cruise Control in Mixed-autonomy Environments

This paper presents a scenario-based hybrid model predictive control (MPC) design approach for cooperative adaptive cruise control (CACC) in mixed-autonomy traffic environments with uncertainties stemming from unexpected maneuvers of human-driven vehicles. Different from the past works that consider one possible realization of uncertainty, the proposed approach here considers multiple scenarios based on the uncertainty description that varies with the relative location of the human-driven vehicles (HVs) and the connected and automated vehicles (CAVs). For each scenario, a mixed integer quadratic programming problem is formulated for the control of CAVs with four operating modes, namely free following, braking, danger, and lane change. Each CAV's operating mode is determined based on the predictive information it receives from its predecessors and the anticipated behaviors of surrounding HVs. All the scenarios are handled simultaneously using the scenario-based MPC approach for a robust CACC. Simulations in a mixed-autonomy traffic system including two lanes demonstrate that the proposed scenario-based hybrid MPC approach significantly reduces deviations from the desired spacing policy and the desired velocity in the platoon during unexpected human-driven vehicle maneuvers, compared with the past work, particularly, a discrete hybrid stochastic (DHSA) MPC approach.