Models Supporting Trajectory Planning in Autonomous Vehicles

Autonomous vehicles have the potential to drastically improve the safety, efficiency and cost of transportation. Instead of a driver, an autonomous vehicle is controlled by an algorithm, offering improved consistency and the potential to eliminate human error from driving: by far the most common cause of accidents. Data collected from different types of sensors, along with prior information such as maps, are used to build models of the surrounding traffic scene, encoding relevant aspects of the driving problem.These models allow the autonomous vehicle to plan how it will drive, optimizing comfort, safety and progress towards its destination. To do so we must first encode the context of the current driving situation: the road geometry, where different traffic participants are, including the autonomous vehicle, and what routes are available to them. To plan the autonomous vehicle's trajectory, we also require models of how other traffic participants are likely to move in the near future, and what risks are incurred for different potential trajectories of the autonomous vehicle. In this thesis we present an overview of different trajectory planning approaches and the models enabling them along with our contributions towards localization, intention recognition, predictive behavior models and risk inference methods that support trajectory planning. Our first contribution is a method that allows localization of anautonomous vehicle using automotive short range radars. Furthermore, we investigate behavior recognition and prediction using models at two different levels of abstraction. We have also explored the integration of two different trajectory planning algorithms and probabilistic environment models which allow us to optimize the expected cost of chosen trajectories.QC 20180327