Maximilian Graf

Maximilian Graf, Oliver Speidel, Jona Ruof et al.

The Institute of Measurement, Control and Microtechnology at Ulm University investigates advanced driver assistance systems for decades and concentrates in large parts on autonomous driving. It is well known that motion planning is a key technology for autonomous driving. It is first and foremost responsible for the safety of the vehicle passengers as well as of all surrounding traffic participants. However, a further task consists in providing a smooth and comfortable driving behavior. In Ulm, we have the grateful opportunity to test our algorithms under real conditions in public traffic and diversified scenarios. In this paper, we would like to give the readers an insight of our work, about the vehicle, the test track, as well as of the related problems, challenges and solutions. Therefore, we will describe the motion planning system and explain the implemented functionalities. Furthermore, we will show how our vehicle moves through public road traffic and how it deals with challenging scenarios like e.g. driving through roundabouts and intersections.

Oliver Speidel, Maximilian Graf, Thanh Phan-Huu et al.

Efficient behavior and trajectory planning is one of the major challenges for automated driving. Especially intersection scenarios are very demanding due to their complexity arising from the variety of maneuver possibilities and other traffic participants. A key challenge is to generate behaviors which optimize the comfort and progress of the ego vehicle but at the same time are not too aggressive towards other traffic participants. In order to maintain real time capability for courteous behavior and trajectory planning, an efficient formulation of the optimal control problem and corresponding solving algorithms are required. Consequently, a novel planning framework is presented which considers comfort and progress as well as the courtesy of actions in a graph-based behavior planning module. Utilizing the low level trajectory generation, the behavior result can be further optimized for driving comfort while satisfying constraints over the whole planning horizon. According experiments show the practicability and real time capability of the framework.