NRI: Addressing Safe Interaction Between Autonomous and Human-Driven Vehicles

互联和自动化车辆是机器人系统，具有显著的计算能力和物理复杂性。他们有能力在没有人为干预的情况下独立地做出情境决策，同时他们在复杂的环境中互动。通过车联网通信的数据和共享信息与重大的技术挑战相关，并使建模和控制的复杂性达到新的水平。预计联网和自动驾驶汽车将逐渐渗透市场，与人类驾驶的车辆进行交互，并应对车辆与一切事物的通信限制，例如，带宽、丢失、错误和延迟。然而，联网和自动化车辆的不同渗透率可以显著改变运输效率和安全性。这笔拨款将协同整合人类驾驶行为与控制理论和学习，开发数据驱动的方法，使互联和自动驾驶车辆的变革性新功能能够安全有效地与人类驾驶车辆进行交互。在教育方面，这项研究是激发人们对科学、技术、工程和数学学科兴趣的绝佳催化剂。这项研究的成果将提供新的方法，以解决互联和自动化车辆的最佳轨迹规划与安全关键控制之间的根本差距。研究框架是在三个相互依赖的维度，即人类驾驶行为，控制理论和学习的交叉点组织的。人类驾驶维度将增强我们对人类驾驶员如何应对不同驾驶场景的理解。控制理论维度将创建有关适当处方函数的知识，这些处方函数将产生关于人类驾驶行为的联网和自动驾驶车辆的最佳决策和规划。学习维度将创建有关联网和自动驾驶车辆如何在遇到与人类驾驶不同的行为时学习调整决策和规划的知识。 因此，这一维度不仅将提高联网和自动驾驶车辆的鲁棒性，还将提高它们在可能遇到的任何不同驾驶行为方面的操作范围。该研究的预期成果旨在使互联和自动驾驶汽车与人类驾驶的汽车协调，以提高安全性并减少污染，能源消耗和旅行延误。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Connected and automated vehicles are robotic systems which exhibit significant levels of computational capability and physical complexity. They have the capacity to make contextually decisions independently, without human intervention, while they interact in a complex environment. The data and shared information through vehicle-to-everything communication are associated with significant technical challenges and gives rise to a new level of complexity in modeling and control. It is expected that connected and automated vehicles will gradually penetrate the market, interact with human-driven vehicles, and contend with vehicle-to-everything communication limitations, e.g., bandwidth, dropouts, errors, and delays. However, different penetration rates of connected and automated vehicles can significantly alter transportation efficiency and safety. This grant will synergistically integrate human-driving behavior with control theory and learning in developing data-driven approaches that will enable a transformative new functionality of connected and automated vehicles to interact with human-driven vehicles safely and efficiently. On the education front and outreach, the research is an excellent catalyst for motivating interest in science, technology, engineering, and mathematics disciplines. The outcome of this research will deliver new methods to address a fundamental gap between optimal trajectory planning of and safe-critical control in connected and automated vehicles. The researched framework is organized at the intersection of three interdependent dimensions, namely, human-driving behavior, control theory, and learning. The human-driving dimension will enhance our understanding on how human drivers will respond to different driving scenarios. The control theory dimension will create knowledge on the appropriate prescription functions that will yield the optimal decisions and planning of connected and automated vehicles with respect to human driving behavior. The learning dimension will create knowledge of how connected and automated vehicles can learn to adapt their decisions and planning in situations where they encounter different behavior from what they already know about human driving. Thus, this dimension will not only improve the robustness of connected and automated vehicles but also their operation range with respect to any different driving behavior that they might encounter. The expected outcome of this research will aim at making connected and automated vehicles to coordinate with human-driven vehicles to improve safety and reduce pollution, energy consumption, and travel delays.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.