A generic driving strategy for urban environments

Autonomous driving in urban environments depends on the ability to interpret the current situation and to react accordingly. This means to continuously make decisions for certain comfort-optimized maneuvers under the constraints of traffic rules and feasibility. This work presents a novel, longitudinal driving strategy formulated as a discrete planning problem. Instead of designing an algorithm for a single one of various potential subproblems, two interfaces are presented, called static and dynamic events, that are capable of representing any situation along the chosen lane of the autonomous vehicle. This allows fast, analytic calculation of Inevitable Collision States which are used as heuristic to realize a guided A* search. Instead of being limited to a small, finite set of maneuvers as rule-based driving strategies like state machines are, the algorithm selects the optimal of an infinite number of possible, implicit maneuvers. The presented algorithm has a worst-case runtime of 80 ms for a planning horizon of 13 seconds and is therefore capable of running online. The approach is evaluated on a simulator in a complex city scenario and on a prototype vehicle on the test track.