Coordination and Cooperation in Self-Driving Vehicles

虽然自动驾驶汽车正在成为现实，但目前面临着几个挑战：它们在速度性能、碰撞预防、传感、合作和协调能力方面受到限制。他们的决策，因此他们的行动，仅仅依赖于他们的船上的感觉和内部控制模型。为了避免碰撞和防止交通拥堵，自动驾驶车辆必须预测其环境中其他车辆（自动驾驶和人工驾驶车辆）的行为，与这些车辆共享其内部状态，并协同操作以选择联合安全有效的控制策略。自动驾驶合作行为的一个原始例子可以在队列行驶中看到，在队列行驶中，一小群卡车排成一个线性结构，使用无线连接来保持彼此之间的规定距离。在此背景下，本研究项目的目标是开发一个框架，以协调自动驾驶车辆车队之间的互动与合作，在复杂的驾驶条件下，在人类驾驶车辆的存在下运行。

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.