Models Supporting Trajectory Planning in Autonomous Vehicles ERIK WARD Doctoral Thesis Stockholm , Sweden 2018

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 an autonomous vehicle using automotive short range radars. These sensors are comparatively cheap, rugged and have the advantage of directly measuring the change in distance using the Doppler effect. We us a simple map representation coupled with the iterative closest point algorithm to achieve results comparable with more complex methods using short range radars for localization. Furthermore, we investigate behavior recognition and prediction using models at two different levels of abstraction. We study classification of driver intentions, modeling high level maneuvers of other drivers, for unsignalized intersection scenarios. To model trajectories in detail, we have studied a probabilistic extension of the Intelligent Driver Model, a car following model first used in traffic simulations. Here we adapt the model for each detected vehicle individually to improve prediction accuracy. To account for uncertainty we can use probabilistic models of the environment, allowing us to represent the inherent uncertainty in predicting the future state of the environment during planning and that can give us better estimates of the risks involved. While there are good theoretical models for planning under uncertainty, in practice we must carefully trade off computational complexity with model complexity to achieve real time algorithms suited for autonomous vehicles. We have explored the integration of two different trajectory planning algorithms and probabilistic environment models which allow us to optimize the expected cost of chosen trajectories. For merge scenarios we evaluated a trajectory planning method where the optimal trajectory is found by enumeration by evaluating a large number of polynomials representing the speed profile of the autonomous vehicle. This allows us to model interactions between the autonomous vehicle and other traffic participants. We also present a lattice based planning approach for general on-road scenarios where trajectories are evaluated based on the probability of dangerous outcomes for a set of modeled situation types.