Coordination and Cooperation in Self-Driving Vehicles

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Although they are becoming tangible reality, Self-driving vehicles currently face several challenges: They are limited in their speed performance, crash prevention, sensing, cooperation, and coordination capabilities. Their decision making, and hence their actions, rely merely on their on-board sensory and internal control models. To avoid crashes and prevent traffic congestion, self-driving vehicles must anticipate the behaviour of other vehicles in their environment (self-driving and human-operated vehicles), share their internal state with these vehicles, and cooperatively operate to choose joint safe and efficient control policies. A primitive example of self-driving cooperative behaviour can be seen in platooning, where a small group of trucks assemble in a linear structure, using wireless connectivity to maintain a prescribed distance between each other. In this context, the goal of this research program is the development of a framework for coordinating interaction and cooperation among a convoy of self-driving vehicles, operating in complex driving conditions in the presence of human-operated vehicles. ***Cooperative sensing and situational assessment capabilities, as well as action cooperation and coordination constitute the building blocks of the proposed framework. Biologically inspired computing will be considered as a strategy for enabling collective sensing, and for facilitating cooperative coordinated driving. The issues of collective sensing, cooperation and coordination will be decomposed into a simple set of behaviors, from which a more complex set of behaviors can be constructed as a function of situation complexity and driving conditions. Game theory will be used as a tool to build an overarching framework for introducing behavior dynamics that motivate cooperation and penalizes selfishness. Various group/collective behavior use-case studies will be conducted to validate the outcome of this research work, both theoretically and using simulations. Examples of use cases of interest include speed harmonization, emergency response, intersection negotiation, crash avoidance, platoons and Convoys. ****** The proposed research program will contribute to the training of Highly Qualified Personnel (3 at the PhD level, 4 at the MSc level, and 10 at the senior undergraduate level.). These students will be provided opportunities to gain knowledge and develop skills in advanced automotive and computing technologies. The program will positively impact road safety by reducing congestions and crashes. It will contribute to creating scientific tools for designing advanced safe-driving capabilities. It will contribute to creating jobs in software manufacturing and will enhance productivity by automating driving and minimizing congestion. Furthermore, this research program will produce technologies that can strengthen the automotive sector in Canada. **