EAGER: A Unified Solution of Mixed Traffic Sensing, Tracking and Acceptable Active Accident Avoidance for On-Demand Automated Shuttles in a Smart City

预计在25年内，65岁及以上的美国人将占总人口的20%左右。随着智慧城市也有望在同一时间内成为现实，开始解决这样一个庞大群体的需求和担忧成为未来智慧城市设计的重要组成部分。在这里，我们特别针对老年人和交通工具有限的人的行动需求。我们考虑多种小型车辆选择，可能提供按需或预定的门到门运输方式。NSF-EAGER项目的重点是研究在密集拥挤的情况下感知和跟踪车辆行人碰撞的潜在来源以及避免碰撞的社会可接受距离的基础研究方面。该项目将为俄勒冈州立大学/哥伦布全球城市团队挑战赛活动SMOOTH（智能移动操作：俄勒冈州立大学交通枢纽）和相关演示提供输入，并帮助开发一个工作系统。这个EAGER项目的关键创新贡献是：开发一个统一的框架，用于在混合交通情况下感知和跟踪，在行人区域内可接受的自动驾驶，以及规避道路机动，以避免与传统的人类驾驶车辆相撞。

It is expected that in 25 years, Americans who are 65 years or older will account for about 20% of the whole population. As smart cities are also expected to become a reality within the same timeframe, starting to address the needs and concerns of such a large group becomes an essential part of the design of a future smart city. Here we specifically address the mobility needs of the elderly and those with limited means of transportation. We consider multiple small vehicle options that might provide on-demand or scheduled means of door-to-door transportation. The NSF-EAGER project focuses on examining basic research aspects of sensing and tracking potential sources of vehicle pedestrian collisions in densely crowded situations and socially acceptable distance for collision avoidance. The project will be providing input to the OSU/Columbus Global City Teams Challenge activity SMOOTH (Smart Mobile Operation: OSU Transportation Hub) and related demonstrations and help develop a working system. The key innovative contributions of this EAGER project are: development of a unifying framework for sensing and tracking in mixed traffic situations, acceptable automated driving within pedestrian zones, and evasive road maneuvering to avoid colliding with conventional human driven vehicles.