Optimal Control and Planning for Autonomous Driving
Abstract
Safety is an emerging tasks in the field of Self-driving Cars that includes Perception, Planning and Decision Making fields to improve the autonomy in all driving conditions especially in the urban driving where the cars shares the environment with other vehicles and pedestrians. Several research activities has been done and some promising results were achieved.
In this master thesis, we have focused on Trajectory planning and Execution task that enables our Amesim Car to overtake safely around predefined environment attempting to reduce the error between the planning and execution. A kinodynamic motion planner like-driver was developed to mimic the human driver actions and to provide us with an executable path.
In addition, an optimal trajectory controller was designed to stabilize the vehicle and track the reference under system constraints. The project combines an RRT-based kinodynamic planner with NMPC-style trajectory control and uses a Siemens Amesim vehicle model for evaluation.
Finally, a co-simulation is carried for different scenarios: double lane change test and racing track with different control schemes.