EAGER: Core Capabilities for Smart Cars

EAGER：智能汽车的核心能力

• 批准号: 1035452
• 负责人: Ragunathan Rajkumar
• 金额: $ 30万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1035452&HistoricalAwards=false
• 关键词: EAGER; Core; Capabilities; Smart; Cars

The fact that 6 vehicles completed a recent Urban Challenge competition, autonomous driving on city streets, clearly demonstrated that autonomous vehicles that can be built to deal with the complexities of driving. The technologies showcased, impressive as they were, need to be significantly enhanced to work dependably in the real world. Vehicles controlled by ever-vigilant cyber-physical systems can lead to significant declines in accidents and resulting deaths/injuries; compensate for human error, distraction, or reduced reaction rates; reduce hours spent in traffic congestion and improve traffic flow; and thereby increase productivity and independence for many segments of the population. However, these platforms and technologies must be deemed robust before widespread adoption can even begin to take place.This EAGER research grant enables a direct and timely interaction between the Carnegie Mellon University research team and the U.S. automotive industry to explore challenges that must be overcome before autonomous and highly-automated vehicles can be safely deployed in real-world driving situations. Challenges include (a) cyber-physical interactions inside the automobile such as ensuring the timely, safe and reliable operations of sensors, actuators, computers, communication buses/networks, electrical power, and cabling interfaces, necessitating a dependable, real-time cyber-physical platform, (b) real-time communications to/from the automobile, using vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) networks to coordinate behaviors with other vehicles when necessary and to obtain/react to information about traffic or accident conditions, traffic lights, and signs, (c) safe behaviors with built-in recovery and safety-enhancement mechanisms to deal with unforeseen road conditions. The research pursues contributions in sensor fusion with dynamic object detection/recognition, safe dynamic behaviors under ever-changing operating conditions, vehicular networks, joint cyber-physical system modeling, analysis/simulation tools, dynamic map-building, end-to-end resource management, and safety-critical real-time fault-tolerant distributed cyber-physical platforms.

事实上，6辆车完成了最近的城市挑战赛，在城市街道上的自动驾驶，清楚地表明，自动驾驶汽车可以用来处理驾驶的复杂性。所展示的技术虽然令人印象深刻，但需要得到显著增强，才能在真实的世界中可靠地工作。由时刻保持警惕的网络物理系统控制的车辆可以显着减少事故和由此造成的死亡/受伤;补偿人为错误、注意力分散或反应速度下降;减少交通拥堵时间并改善交通流量;从而提高许多人群的生产力和独立性。 然而，这些平台和技术必须被认为是强大的，才能被广泛采用，甚至可以开始发生。EAGER研究资助使卡内基梅隆大学研究团队和美国之间的直接和及时的互动。汽车行业，探索在自动驾驶和高度自动化的车辆可以安全地部署在现实世界的驾驶情况之前必须克服的挑战。挑战包括（a）汽车内部的网络物理交互，例如确保传感器、执行器、计算机、通信总线/网络、电源和布线接口的及时、安全和可靠的操作，需要一个可靠的、实时的网络物理平台，（B）与汽车的实时通信，使用车辆对车辆（V2 V）和车辆对基础设施（V2 I）网络在必要时与其他车辆协调行为，并获得有关交通或事故状况、交通灯和标志的信息/对这些信息做出反应，（c）安全行为，内置恢复和加强安全的机制，以应对不可预见的道路情况。 该研究致力于传感器融合与动态目标检测/识别，不断变化的操作条件下的安全动态行为，车辆网络，联合网络物理系统建模，分析/仿真工具，动态地图构建，端到端资源管理和安全关键的实时容错分布式网络物理平台。