Autonomous Lane Change Control Using Proportional-Integral-Derivative Controller and Bicycle Model

As advanced vehicle controls and autonomy become mainstream in the automotive industry, the need to employ traditional mathematical models and control strategies arises for the purpose of simulating autonomous vehicle handling maneuvers. This study focuses on lane change maneuvers for autonomous vehicles driving at low speeds. The lane change methodology uses PID (Proportional-Integral-Derivative) controller to command the steering wheel angle, based on the yaw motion and lateral displacement of the vehicle. The controller was developed and tested on a bicycle model of an electric vehicle (a Chevrolet Bolt 2017), with the implementation done in MATLAB/Simulink. This simple mathematical model was chosen in order to limit computational demands, while still being capable of simulating a smooth lane change maneuver under the direction of the car’s mission planning module at modest levels of lateral acceleration. The simulation indicated that the lane change control system performed well for low speeds and at moderate steering wheel angles. After the simulation phase, the model was converted to implementable vehicle code and integrated into a vehicle for on-road testing of obstacle avoidance. Physical validation of the control strategy showed that it can be used successfully to perform smooth lane changes in an autonomous driving environment.