PFI:BIC: Smart Human-Centered Collision Warning System: sensors, intelligent algorithms and human-computer interfaces for safe and minimally intrusive car-bicycle interactions

PFI:BIC：以人为中心的智能碰撞预警系统：传感器、智能算法和人机界面，实现安全且侵入性最小的汽车与自行车交互

• 批准号: 1631133
• 负责人: Rajesh Rajamani
• 金额: $ 99.97万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1631133&HistoricalAwards=false
• 关键词: PFI; BIC; Smart; Human; Centered

This research project will develop a smart warning system to enable safe and minimally intrusive interactions between motorists and bicycles. Interactions with bicycles are rare for a typical motorist, therefore safety-conscious drivers naturally focus on other motor vehicles in the roadway, and may not become aware of the presence of a bicycle until it is too late. In contrast, interactions with motor vehicles are commonplace for a bicyclist. Furthermore, the bicyclist faces far greater consequences in an accident than a motorist. Therefore it is appropriate for a collision prevention system to be the responsibility of the cyclist. Continuous display of bright flashing lights or loud sounds may suffice to bring attention to the cyclist, but they may unnecessarily distract nearby motorists, or they may alarm passing drivers, and cause them to move dangerously far from their own lane. The system under development will guide motorists to pass bicycles with exactly as much distance as safety requires. Furthermore, it will provide alerts only to those drivers that have a significant probability of collision with the bicycle. The system to be developed will incorporate a knowledge base of likely collision scenarios, thus minimizing false alarms. The system will provide guidance cues to the bicyclist, to ensure a safe and respectful response to motor vehicles. Human factors studies will be used to design an alert system that provides motorists with specific and effective audio-visual cues. These studies will also be used to ensure that cyclists do not respond to the enhanced security by becoming more reckless. It is expected that the technology developed in this project will enable motorists to interact with bicycles safely and with minimal intrusion. It will reduce the approximately 48,000 bicyclist injuries and 700 fatalities that occur every year. The development of a bicycle-mounted collision avoidance system must address a number of challenges beyond those required for a similar system on a car. These challenges include the need to address more complex collision scenarios, the need to provide alerts to the drivers of other vehicles, the need for inexpensive, light and smaller sensors, and the need to rely on human users for effective functioning of the system. These challenges will be addressed by development of unique custom-designed sensors, novel estimation algorithms for vehicle tracking and use of a rigorous human factors study to determine which warning systems will be effective and how such warnings should be provided to the involved motorist and bicyclist in real-world traffic scenarios. The warning presentation is designed to minimize the trade-offs between low reaction time and unnecessarily intrusive disturbances to nearby motorists. The custom sensors developed in the project include a triad sonar transducer unit for side vehicles, and front and rear laser sensors on real-time controlled rotational laser platforms to track vehicles at continuously changing lateral and longitudinal distances. The human factors studies in the project will enhance our understanding of human behavior in multi-modal collision avoidance systems and analyze possible long-term changes in behavior after prolonged use of the system. The project also includes an intensive 6-month field operational test in collaboration with an industrial partner to evaluate the effectiveness of the developed technology. The field operational tests will involve 10 bicycles, bicyclist volunteers with significant daily urban commutes and extensive analysis of bicycle data recorded in real-world traffic conditions. Due to the close industrial collaboration, the research conducted in this project will accelerate the path to commercialization of this smart system with its potential benefits to the country. The project will educate two graduate students and a post-doctoral researcher, providing them experience in inter-disciplinary research as well as an opportunity for strong industrial interaction.This project is a collaboration between The University of Minnesota (Mechanical Engineering, Computer Science and Human Factors Engineering/Psychology) and primary industrial partner Quality Bicycle Products (QBP), (Bloomington, Minnesota, Large business). Broader context partners include The Minneapolis Bicycle Coalition, (Minneapolis, MN, nonprofit).

该研究项目将开发一种智能警告系统，以实现驾驶员和自行车之间的安全和最低限度的侵入性互动。对于典型的驾驶者来说，与自行车的互动是罕见的，因此具有安全意识的驾驶员自然会关注道路上的其他机动车辆，并且可能直到为时已晚才意识到自行车的存在。相比之下，与机动车辆的交互对于骑自行车的人来说是司空见惯的。此外，骑自行车的人在事故中面临的后果比开车的人大得多。因此，碰撞预防系统由骑自行车的人负责是适当的。连续亮起的闪光灯或响亮的声音可能足以引起骑自行车者的注意，但它们可能会不必要地分散附近驾驶者的注意力，或者可能会警告路过的司机，并导致他们危险地远离自己的车道。正在开发的系统将引导驾车者在安全要求的距离内通过自行车。此外，它将只向那些有很大可能与自行车相撞的驾驶员提供警报。拟开发的系统将包括一个关于可能碰撞情况的知识库，从而尽量减少误报。该系统将为骑自行车的人提供指导提示，以确保对机动车辆的安全和尊重。人为因素的研究将用于设计一个警报系统，为驾驶者提供具体和有效的视听提示。这些研究还将用于确保骑自行车的人不会因为加强安全而变得更加鲁莽。预计该项目开发的技术将使驾驶者能够安全地与自行车互动，并将干扰降至最低。它将减少每年发生的大约48，000起骑自行车的人受伤和700起死亡事件。自行车安装的防撞系统的开发必须解决许多挑战，超出了汽车上的类似系统所需的挑战。这些挑战包括需要解决更复杂的碰撞场景，需要向其他车辆的驾驶员提供警报，需要廉价，轻便和更小的传感器，以及需要依靠人类用户来实现系统的有效功能。这些挑战将通过开发独特的定制设计的传感器，用于车辆跟踪的新型估计算法以及使用严格的人为因素研究来解决，以确定哪些警告系统将是有效的，以及如何在现实世界的交通场景中向相关的驾驶员和骑自行车的人提供此类警告。警告显示的目的是尽量减少低反应时间和不必要的干扰附近的驾驶者之间的权衡。该项目中开发的定制传感器包括用于侧载车辆的三元组声纳传感器单元，以及实时控制旋转激光平台上的前后激光传感器，以连续变化的横向和纵向距离跟踪车辆。该项目中的人为因素研究将增强我们对多模式防撞系统中人类行为的理解，并分析长期使用该系统后可能发生的长期行为变化。该项目还包括与工业合作伙伴合作进行为期6个月的密集现场操作测试，以评估所开发技术的有效性。现场操作测试将涉及10辆自行车，每天有大量城市通勤的自行车志愿者，以及对真实交通条件下记录的自行车数据的广泛分析。由于密切的工业合作，该项目中进行的研究将加速这一智能系统的商业化道路，并为该国带来潜在的利益。该项目将培养两名研究生和一名博士后研究员，为他们提供跨学科研究的经验以及强大的工业互动的机会。该项目是明尼苏达大学（机械工程，计算机科学和人为因素工程/心理学）和主要工业合作伙伴质量自行车产品（QBP）（布卢明顿，明尼苏达州，大型企业）之间的合作。更广泛的合作伙伴包括明尼阿波利斯自行车联盟（明尼阿波利斯，明尼苏达州，非营利组织）。

项目成果

Resilient Control Under Cyber-Attacks in Connected ACC Vehicles

• DOI: 10.1115/dscc2019-9096
• 发表时间: 2019-11
• 作者: Woongsun Jeon;A. Zemouche;R. Rajamani

On-Bicycle Vehicle Tracking at Traffic Intersections Using Inexpensive Low-Density Lidar

使用廉价的低密度激光雷达在交通路口跟踪自行车车辆

• DOI: 10.23919/acc.2019.8814442
• 发表时间: 2019
• 期刊: 2019 American Control Conference
• 作者: Xie, Zhenming;Rajamani, Rajesh
• 通讯作者: Rajamani, Rajesh

On the need for switched-gain observers for non-monotonic nonlinear systems

• DOI: 10.1016/j.automatica.2020.108814
• 发表时间: 2020-04
• 期刊: Autom.
• 作者: R. Rajamani;Woongsun Jeon;H. Movahedi;A. Zemouche

Smartphone localization inside a moving car for prevention of distracted driving

• DOI: 10.1080/00423114.2019.1578889
• 发表时间: 2020-02-01
• 期刊: VEHICLE SYSTEM DYNAMICS
• 影响因子: 3.6
• 作者: Johnson, Gregory;Rajamani, Rajesh
• 通讯作者: Rajamani, Rajesh

Rear Vehicle Tracking on a Bicycle Using Active Sensor Orientation Control

• DOI: 10.1109/tits.2017.2764006
• 发表时间: 2018-08-01
• 期刊: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
• 影响因子: 8.5
• 作者: Jeon, Woongsun;Rajamani, Rajesh
• 通讯作者: Rajamani, Rajesh