Graph-Based Speed Planning for Autonomous Driving
Motion Planning in autonomous driving defines the task of planning the desired movement of a vehicle through a dynamically moving environment. A plan is stored as trajectory, saving spatial and temporal information about the future vehicle movement. Path-Speed decomposition is a planning method for finding such a trajectory. A path is planned in a first step, followed by an according speed profile.
This aims to implement and evaluate a planner for finding a rough speed profile in a descretized search space. A graph is created and a single source shortest paths algorithm is used to find the optimal speed profile within the limited search space, evaluated by cost functions representing the requirements of speed planning. The rough speed profile can serve as initial solution for numerical optimization, which is not part of this thesis.
The implemented approach is evaluated in sumulation of various urban traffic scenarios, showing promising collision free and low-jerk trajectories. It is able to find a speed profile in real-time. Therefore, the planner seems useful for practical application in an autonomous driving vehicle.