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

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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).