Model Predictive Control-Based Trajectory Optimization for Autonomous Vehicles Using Risk-Aware Corridors

In this paper, we focus on trajectory planning for lane change maneuvers and elaborately consider the impacts of dynamic uncertainties from surrounding vehicles. First, a risk-aware corridor is developed to guarantee that the collision probability is below an acceptable risk level. In contrast to the existing studies, a more succinct and intuitive corridor is constructed based on an explicit safety check theorem. Since it relies solely on the distances between autonomous vehicles and surrounding ones, such a corridor can be easily converted into polytopic constraints and seamlessly integrated with an optimization scheme. Furthermore, a computationally tractable and hierarchical scheme is designed to decide the optimal merging instant and generate the expected trajectory. In this scheme, the trade-off between optimality and complexity in calculating merging instants can be well balanced through feasibility estimation and optimum searching. Furthermore, the trajectory is optimized by model predictive control (MPC) technique. Finally, both numerical simulations and naturalistic validations are conducted to verify the effectiveness. The comparison results indicate the convincing superiority of our scheme in achieving safer driving in an uncertain environment. The code is now available athttps://github.com/Aeolson/code_otp.git