Predictive control of an autonomous vehicle to reduce traffic instability

Traffic flow is known to be unstable at high densities, creating undesirable stop-and-go waves. Using real-time data and a long-range perception, an automated vehicle (AV) could detect perturbations early and adapt its trajectory to keep a nearly constant speed, making the overall flow smoother. This paper presents the following algorithm. Using a short-time prediction of its leader's trajectory, the AV computes a future trajectory that minimizes speed variations while keeping both a bounded acceleration and jerk. This controled trajectory is updated in real-time until the perturbation is passed and the vehicle goes back to its normal behavior. Simulation results using human drivers data for the downstream traffic show that the speed variations is reduced by more than 20 %.