Maneuver planning for highly automated vehicles

摘要

One important aspect of autonomous driving lies in the selection of maneuver sequences. Here the challenge is to optimize the driving comfort and travel-duration, while always keeping within the safety limits. Human drivers analyze and try to anticipate the traffic situation choosing their actions not only based on current information but also based on experience. The decision making process can be treated as a planning problem. Classical planning systems consider the autonomous driving task as a global numeric optimization problem, which in populated dynamic environments can become computationally intractable. In addition, purely numeric computations hamper the understanding of the decision making for the human user. We propose a planning system that presents a multi-level architecture, similar to the human reasoning process, which combines continuous planning with semantic information. This allows the planning system to deal with the complexity of the problem in a computationally efficient way and also provides an intuitive interface to communicate the decisions to the driver. We validate our approach in simulation and through a set of experiments carried out with a real vehicle and an integrated traffic simulation also known as vehicle in the loop (VIL).