Autonomous lateral maneuvers for self-driving vehicles in complex traffic environment

Autonomous driving functions have gained great interests from both academia and industry over the years. This paper proposes a Model Predictive Control based method to generate a safe and feasible trajectory for the ego vehicle to perform various lateral maneuvers and to produce optimized control inputs to guide the ego vehicle through a mixed traffic environment with both human drivers and autonomous vehicle. A novel reference speed generation function is proposed to automatically adjust the position of the ego vehicle before the initiation of any lateral maneuvers. After a proper gap is selected, the lateral maneuver initiation function with an add-on threshold function is introduced to ensure the safety of the maneuver and to enable the ego vehicle to automatically start or stop the lateral maneuver. A nonlinear and high fidelity tire model is utilized in the simulation to validate the algorithm and the simulation results show that various maneuvers can be successfully achieved without re-tuning the weights or changing the parameters of the controller.