Coordination and Cooperation in Self-Driving Vehicles

尽管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，以及他们的行动，仅仅依赖于他们的板载感官和内部控制模型。为了避免碰撞和交通拥堵，自动驾驶汽车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，并合作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中看到，一小群卡车聚集在线性结构中，使用无线连接来保持彼此之间的规定距离。在这种情况下，该研究计划的目标是开发一个框架，用于协调自动驾驶车辆车队之间的互动和合作，在人类操作车辆存在的复杂驾驶条件下运行。* 合作感知和情况评估能力以及行动合作和协调构成拟议框架的组成部分。 生物启发计算将被视为一种策略，使集体感知，并促进合作协调驾驶。集体感知、合作和协调的问题将被分解为一组简单的行为，从中可以构建一组更复杂的行为，作为情况复杂性和驾驶条件的函数。博弈论将被用来作为一种工具，以建立一个总体框架，引入行为动力学，激励合作和惩罚自私。将进行各种群体/集体行为用例研究，以验证这项研究工作的结果，无论是理论上还是使用模拟。感兴趣的用例示例包括速度协调、紧急响应、交叉口协商、碰撞避免、队列和车队。 ** 拟议的研究计划将有助于培养高素质的人才（3名博士，4名硕士和10名高级本科生）。这些学生将有机会获得先进汽车和计算技术方面的知识和技能。该计划将通过减少交通事故和撞车事故对道路安全产生积极影响。它将有助于创建设计先进安全驾驶能力的科学工具。它将有助于在软件制造业创造就业机会，并将通过自动驾驶和最大限度地减少拥堵来提高生产力。此外，该研究计划将产生可以加强加拿大汽车行业的技术。 **

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. **