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EAGER: Real-Time: Intelligent Intersections

EAGER：实时：智能交叉口

基本信息

批准号：
1839843
负责人：
Pravin Varaiya
金额：
$ 29.99万
依托单位：
University of California-Berkeley
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-15 至 2020-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839843&HistoricalAwards=false
关键词：
EAGER Real Time Intelligent Intersections

项目摘要

Intersections are dangerous. Threats arise from interactions among pedestrians, bicycles and vehicles; traffic signal operation that does not reveal who has the right of way; blind spots from vehicle obstructions; and illegal movements. These threats cannot be avoided by autonomous vehicles, human drivers, cyclists and pedestrians who do not have the information they need to avoid wrong decisions, leading to crashes. This missing information can be calculated and communicated by an intelligent intersection. The information gives the full signal phase, an estimate of when the phase will change, blind spot occupancy, and detection of red-light violators. The proposed research will develop the real-time learning and decision algorithms of the intelligent intersection.The euphoria over the rise of the autonomous vehicle industry has concealed the fact that the safety record of autonomous vehicles today is much worse than that of human drivers. Nor is it acknowledged that the safety of autonomous vehicles and others (drivers, cyclists and pedestrians) can be improved by information generated from the infrastructure. The proposed research aims to counter these misconceptions by showing how intersection safety can be enhanced by infrastructure assistance. The research is motivated by current concerns and grounded on assumptions that are valid today. The U.S. registered 40,327 traffic deaths in 2016, an increase of 13.5% since 2010. Our aim is to make a dent in this national health emergency by helping to avoid errors that can be prevented with better intelligence. California's DMV has begun to issue permits to test vehicles without a safety driver. DMV regulations give local cities some latitude in determining the conditions under which such tests are permitted. The safety of their citizens will be of foremost concern to local authorities. This research will help them in their decisions and suggest the kind of information that they may require from tests conducted in their cities.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.

十字路口很危险。威胁来自行人、自行车和车辆之间的相互作用;交通信号灯的运作没有显示谁有通行权;车辆障碍造成的盲点;以及非法移动。自动驾驶汽车、人类驾驶员、骑自行车的人和行人无法避免这些威胁，因为他们没有避免错误决策所需的信息，从而导致撞车事故。这些缺失的信息可以通过智能交叉口进行计算和传达。这些信息给出了完整的信号相位，相位何时改变的估计，盲点占用率和闯红灯者的检测。这项研究将开发智能交叉路口的实时学习和决策算法。自动驾驶汽车行业的兴起掩盖了一个事实，即今天自动驾驶汽车的安全记录比人类驾驶员差得多。也没有人承认，自动驾驶汽车和其他人（司机、骑自行车的人和行人）的安全性可以通过基础设施产生的信息来提高。拟议的研究旨在通过展示如何通过基础设施援助来增强交叉口安全来消除这些误解。这项研究的动机是当前的问题，并基于今天有效的假设。2016年美国交通事故死亡人数为40,327人，比2010年增加了13.5%。我们的目标是通过帮助避免可以通过更好的情报来预防的错误，从而在这一国家卫生紧急情况中取得进展。加州的DMV已经开始发放许可证，测试没有安全驾驶员的车辆。DMV的规定给了当地城市一些自由，以确定在何种条件下允许这种测试。公民的安全将是地方当局最关心的问题。这项研究将帮助他们做出决定，并建议他们可能需要从他们所在城市进行的测试中获得的信息。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。