Pro-social control of connected automated vehicles in mixed-autonomy multi-lane highway traffic

We propose pro-social control strategies for connected automated vehicles(CAVs)to mitigate jamming waves in mixed-autonomy multi-lane traffic,resulting from car-following dynamics of human-driven vehicles(HDVs).Different from existing studies,which focus mostly on ego vehicle objectives to control CAVs in an individualistic manner,we devise a pro-social control algorithm.The latter takes into account the objectives(i.e.,driving comfort and traffic efficiency)of both the ego vehicle and surrounding HDVs to improve smoothness of the entire observable traffic.Under a model predictive control(MPC)framework that uses acceleration and lane change sequences of CAVs as optimization variables,the problem of individualistic,altruistic,and pro-social control is formulated as a non-convex mixed-integer nonlinear program(MINLP)and relaxed to a convex quadratic program through converting the piece-wise-linear constraints due to the optimal velocity with relative velocity(OVRV)car-following model into linear constraints by introducing slack variables.Low-fidelity simulations using the OVRV model and high-fidelity simulations using PTV VISSIM simulator show that pro-social and altruistic control can provide significant performance gains over individualistic driving in terms of efficiency and comfort on both single-and multi-lane roads.