Cooperative Lane Changing in Mixed Traffic Can Be Robust to Human Driver Behavior

We derive time and energy-optimal control policies for a Connected Autonomous Vehicle (CAV) to complete lane change maneuvers in mixed traffic. The interaction between CAVs and Human-Driven Vehicles (HDVs) requires the best possible response from a CAV to actions by its neighboring HDVs. This interaction is formulated using a bilevel optimization setting with an appropriate behavioral model for an HDV. An iterated best response (IBR) method is then used to determine a Nash equilibrium. However, we also show that CAV cooperation can eliminate or greatly reduce the interaction between CAVs and HDVs. We derive a simple threshold-based criterion to select an optimal policy for the lane-changing CAV to merge ahead of a cooperating CAV in the target lane. In this case, the trajectory of the lane-changing CAV is independent of HDV behavior. Simulation results are included to demonstrate the effectiveness of our CAV controllers in terms of minimizing cost and disruption to traffic flow while guaranteeing safety when uncontrollable HDVs are present.