Trajectory planning for traffic intersection scenario using model predictive control with control barrier function constraints

For autonomous vehicles traveling in intersections, it is essential to plan trajectories that ensure safety and improve road efficiency. This paper proposes a trajectory planning and motion control scheme for intersections based on model predictive control and control barrier function (MPC-CBF). An expanded elliptical model is constructed to define a danger zone for CBF constraints, enhancing safety and reducing stop-and-go delays. To balance safety and path planning feasibility, the decay coefficient of CBF is treated as an optimisable variable in the MPC optimisation problem. The real-time adjusted decay coefficient dynamically changes the safety constraint range, preventing over-avoidance. Additionally, Bayesian optimisation trains the weights of the multi-objective problem in typical intersection conflict scenarios. The weights are selected based on the relative position between the autonomous and obstacle vehicles. Finally, the control scheme is evaluated using Carsim and Matlab/Simulink co-simulation.