Gain-scheduled model predictive controller for vehicle-following trajectory generation for autonomous vehicles

As the development of autonomous vehicles accelerates, the need to enhance the comfort characteristics for those vehicles has become important. In the present article, an enhanced vehicle-following motion planner algorithm is presented. The aim of the algorithm is to smoothen the repetitive braking and acceleration behaviour during vehicle following in traffic jam situations. The algorithm uses the information gathered from Lidar sensor, cameras and vehicle-embedded sensors in real time to construct the range vs. range-rate diagram, and it computes the desired velocity trajectory for the speed controller. The algorithm is based on the Gain-Scheduled Model Predictive Controller (MPC), where at least one MPC controller is designed to handle one of the three vehicle-following operating conditions: speed control, headway control and emergency brake control. The algorithm allows the designer to manipulate two vehicle following variables: standstill distance between lead vehicle and ego vehicle, and the headway time gap. The algorithm is experimentally validated on a full-size passenger vehicle.