MPC Based Vehicular Trajectory Planning in Structured Environment

In this paper, the hierarchical architecture of trajectory planning and control is set up for safe driving with multiple participants without collision, where both levels utilize a time-varying model predictive control methodology. Firstly, a high-level planner formulates an optimal control problem to obtain an optimal trajectory while satisfying different constraints. In particular, due to obstacles' occupation, several partition functions are generated as linear collision constraints through an optimization process in order to convexify the collision-free region into sub-regions. Secondly, the low-level controller receives the desired trajectory from the high-level planner, and then computes an appropriate steering angle to execute the planned maneuver. Both levels are formulated within the model predictive control(MPC) methodology. The strength of this framework is that it combines different constraints in each optimal control problem. Including a high-level planner ensures the feasibility of safe trajectory planning and the use of a low-level controller ensures tracking stability for safe driving, even under various collision constraints and model mismatch between system plant and predictive process model. Finally, several simulations verified the proposed framework, which was used to compute an optimal, safe trajectory over a set of static or moving obstacles and stabilize the vehicle around it.