Integrated nonlinear model predictive control for automated driving

Abstract This work presents a Nonlinear Model Predictive Control (NMPC) scheme to perform evasive maneuvers and avoid rear-end collisions. Rear-end collisions are among the most common road fatalities. To reduce the risk of collision, it is necessary for the controller to react as quickly as possible and exploit the full vehicle maneuverability (i.e., combined control of longitudinal and lateral dynamics). The proposed design relies on the simultaneous use of steering and braking actions to track the desired reference path and avoid collisions with the preceding vehicle. A planar vehicle model was used to describe the vehicle dynamics. In addition, the dynamics of the brake system were included in the NMPC prediction model. Furthermore, the controller incorporates constraints to ensure vehicle stability and account for actuator limitations. In this respect, the constraints were defined on Kamm circle and Ideal Brake Torque Distribution (IBD) logic for optimal tire force and brake torque distribution. To evaluate the design, the performance of the proposed NMPC was compared with two ”more classical” MPC designs that rely on: (i) a linear bicycle model, and (ii) a nonlinear bicycle model. The performance of these three controller designs was evaluated in simulation (using a high-fidelity vehicle simulator) via relevant KPIs, such as reference tracking Root Mean Square (RMS) error, controller’s rise/settling time, and Distance to Collision (i.e., the lateral distance by which collision was avoided safely). Different single-lane-change maneuvers were tested and the behavior of the controllers was evaluated in the presence of lateral wind disturbances, road friction variation, and maneuver aggressiveness.