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

该研究项目将开发一种智能预警系统，使驾驶者和自行车之间的互动安全且干扰最小。对于一个典型的驾车者来说，与自行车的互动是很少的，因此有安全意识的司机自然会把注意力集中在道路上的其他机动车辆上，可能不会意识到自行车的存在，直到为时已晚。相比之下，对于骑自行车的人来说，与机动车辆的互动是司空见惯的。此外，在事故中，骑自行车的人比开车的人面临更大的后果。因此，碰撞预防系统应由骑车人负责。连续不断地闪烁明亮的灯光或发出响亮的声音可能足以引起骑车人的注意，但它们可能会不必要地分散附近的汽车司机的注意力，或者可能会使过往的司机感到惊慌，导致他们危险地远离自己的车道。这个正在开发的系统将引导驾车者在安全要求的范围内通过自行车。此外，它将只向那些有很大可能与自行车相撞的司机提供警报。即将开发的系统将包含一个可能发生碰撞的知识库，从而最大限度地减少误报。该系统将为骑自行车的人提供引导提示，以确保对机动车辆的安全和尊重。人为因素研究将用于设计警报系统，为驾驶者提供具体而有效的视听提示。这些研究还将用于确保骑自行车的人不会因为加强了安全措施而变得更加鲁莽。预计该项目开发的技术将使驾车者能够安全地与自行车互动，并将干扰降到最低。它将减少每年大约48,000起自行车受伤事件和700起死亡事件。自行车防撞系统的开发必须解决许多汽车防撞系统所面临的挑战。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有效运行系统。这些挑战将通过开发独特的定制设计的传感器、新颖的车辆跟踪估计算法和使用严格的人为因素研究来解决，以确定哪种警告系统是有效的，以及如何在现实交通场景中向相关的驾驶者和骑自行车的人提供这些警告。该警告演示的目的是尽量减少低反应时间和对附近驾驶者不必要的干扰之间的权衡。项目中开发的定制传感器包括用于侧车的三位一体声纳换能器单元，以及实时控制旋转激光平台上的前后激光传感器，用于在不断变化的横向和纵向距离上跟踪车辆。该项目的人为因素研究将增强我们对多模态避碰系统中人类行为的理解，并分析长期使用系统后可能出现的长期行为变化。该项目还包括与工业伙伴合作进行为期6个月的密集现场操作测试，以评估所开发技术的有效性。实地操作测试将涉及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进行广泛分析。由于密切的工业合作，在这个项目中进行的研究将加速这一智能系统的商业化道路，并为国家带来潜在的利益。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，并为他们提供强大的产业互动机会。该项目是明尼苏达大学（机械工程，计算机科学和人因工程/心理学）和主要工业合作伙伴Quality Bicycle Products (QBP) （Bloomington, Minnesota，大型企业）之间的合作项目。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）。

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).