Real-Time Decision Making by Driverless City Vehicles A Discrete Event Driven Approach

This thesis addresses the topic of real-time decision making by driverless (autonomous) city vehicles, i.e. their ability to make appropriate driving decisions in non-simplified urban traffic conditions. After addressing the state of research, and explaining the research question, the thesis presents solutions for the subcomponents which are relevant for decision making with respect to information input (World Model), information output (Driving Maneuvers), and the real-time decision making process. The World Model is a software component developed to fulfill the purpose of collecting information from perception and communication subsystems, maintaining an up-to-date view of the vehicle’s environment, and providing the required input information to the Real-Time Decision Making subsystem in a well-defined, and structured way. The real-time decision making process consists of two consecutive stages. While the first decision making stage uses a Petri net to model the safetycritical selection of feasible driving maneuvers, the second stage uses Multiple Criteria Decision Making (MCDM) methods to select the most appropriate driving maneuver, focusing on fulfilling objectives related to efficiency and comfort. The complex task of autonomous driving is subdivided into subtasks, called driving maneuvers, which represent the output (i.e. decision alternatives) of the real-time decision making process. Driving maneuvers are considered as implementations of closed-loop control algorithms, each capable of maneuvering the autonomous vehicle in a specific traffic situation. Experimental tests in both a 3D simulation and real-world experiments attest that the developed approach is suitable to deal with the complexity of real-world urban traffic situations. Acknowledgements First, I would like to thank Professor Ljubo Vlacic for his support, encouragement, and valuable advice during my time at ICSL. I would also like to thank INRIA’s team IMARA for the financial support provided towards conducting the experimental work at their test track in Rocquencourt, and I am particularly grateful to Dr. Michel Parent, Laurent Bouraoui and Francois Charlot for their effort and assistance in performing the experiments. Special thanks go to the students who supported me in implementing various components of the autonomous vehicle control software: Sebastian Boisse, Evan Baxter, Dan Boers, David Cook, and Brett Wood.