Lane-Change Collision Avoidance Control for Automated Vehicles with Control Barrier Functions

In congested street or highway scenarios, such as lane-change in dynamically changing traffic flow situations, the planning and tracking of trajectories for connected and automated vehicles (CAVs) represent some of the most challenging tasks. As an introduction to automated lane-change in CAVs, this paper presents a control Lyapunov function (CLF) approach that follows an optimal desired trajectory while observing the constraints imposed by the control barrier function (CBF) in order to avoid collision with surrounding vehicles. By combining CLF and CBF within the framework of quadratic programs (QP), it allows the implementation to simultaneously track the control objective (represented by the CLF) and satisfy the constraints of the desired state of the system (represented by the CBF). Therefore, it provides the possibility of tracking simultaneously the path of the unmanned vehicle, within the constraints of the surrounding vehicles as well as the surrounding environment. From simulations and comparison results, the controller presented here can perform collision avoidance well and can be used on a real traffic system, which has the advantage of providing faster and more precise lane-change results than another work.