
NSF Org: |
TI Translational Impacts |
Recipient: |
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Initial Amendment Date: | August 29, 2016 |
Latest Amendment Date: | February 13, 2019 |
Award Number: | 1631133 |
Award Instrument: | Standard Grant |
Program Manager: |
Jesus Soriano Molla
jsoriano@nsf.gov (703)292-7795 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
Start Date: | September 1, 2016 |
End Date: | August 31, 2020 (Estimated) |
Total Intended Award Amount: | $999,733.00 |
Total Awarded Amount to Date: | $1,015,733.00 |
Funds Obligated to Date: |
FY 2019 = $16,000.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
2221 UNIVERSITY AVE SE STE 100 MINNEAPOLIS MN US 55414-3074 (612)624-5599 |
Sponsor Congressional District: |
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Primary Place of Performance: |
111 Church Street SE Minneapolis MN US 55455-2070 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
PFI-Partnrships for Innovation, Dynamics, Control and System D |
Primary Program Source: |
01001920DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.084 |
ABSTRACT
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).
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project developed a collision prevention system for bicycles based on tracking trajectories of nearby cars and sounding a loud horn to alert the car driver to the presence of the bicycle. In particular, three types of collisions were considered: rear collision from a car behind the bicycle, collision from a right-turning car next to the bicycle, and collision from a left-turning car in a traffic intersection. Inexpensive laser-based sensors were utilized for addressing all three collision scenarios. The decision to focus on these three types of collisions was motivated by bicycle-car crash statistics which showed that approximately 40% of fatal bicycle-car crashes are rear collisions and that over 70% of fatal crashes occur at or within 50 feet of traffic intersections.
The fundamental research conducted in the project included development of active sensing algorithms to enable thin beam laser sensors to continuously track moving vehicles, development of nonlinear observer design algorithms for vehicle tracking applications, and development of switched gain hybrid observers for state estimation in non-monotonic nonlinear systems.
The products from the project include a large number of journal publications and refereed conference papers, and one patent application that has been granted by the US Patents Office. The research team plans to further pursue commercialization of the smart bicycle technology and is in conversation with two different bicycle companies for licensing the patent obtained by the research team.
The societal impact of the project includes development of technology for improving bicycle safety and potentially reducing the 50,000 injuries that occur every year in the US in car-bicycle collisions. In addition, the project could lead to commercialization of research technology developed in the project.
The broader impacts of the project include education of two Ph.D., one M.S. and four undergraduate students, outreach to high school students, publicity of the developed technology through a news show broadcast multiple times on the local Fox News channel, and through a video presentation at the awards ceremony of a major global intelligent transportation systems conference.
Last Modified: 10/20/2020
Modified by: Rajesh Rajamani
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