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STTR Phase I: Internet of Things (IOT) Safety Device and System for Micro-Mobility Products

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2232899
Agency Tracking Number: 2232899
Amount: $275,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: I
Solicitation Number: NSF 22-551
Timeline
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-04-01
Award End Date (Contract End Date): 2024-03-31
Small Business Information
1674 snowden cr
Rochester hills, MI 48306
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 mutasim salman
 (248) 895-9296
 masalman1122@gmail.com
Business Contact
 mutasim salman
Phone: (248) 895-9296
Email: masalman1122@gmail.com
Research Institution
 Regents of the University of Michigan - Dearborn
 
4901 EVERGREEN RD
Dearborn, MI 48128
United States

 Nonprofit College or University
Abstract

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is the development of new technologies to enhance the safety of micro-mobility vehicles and fleets (bicycles, electronic bikes, and electric scooters). Micro-mobility has a high potential to reduce congestion, emissions, and noise pollution in urban settings.These vehicles can address many first- and last-mile transportation challenges. The technology developed in this project will integrate low-cost sensors, advanced machine learning and model-based algorithms with Internet of Things (IOT)-based technologies for micro-mobility safety devices. Furthermore, IOT-based early detection and warning systems can address safety concerns in the use of micro-mobility, resulting in the development of strong ecosystems._x000D_
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This Small Business Technology Transfer (STTR) Phase I project will develop and evaluate a cost-effective. innovative, IOT technology and turn it into a product and service that are essential to the safety and reliability of the micro-mobility vehicles and fleets. The technology involves predicting likely future failures in vehicle braking components and systems in advance of their occurrence as well as early detection of hazardous driving conditions (due to misbehavior of riders, road conditions, or weather conditions).The solution will issue warnings to the rider and proactive alerts with actionable recommendations (e.g., for proactive maintenance). The project will develop and evaluate model-based and machine-learning-assisted algorithms for the detection, isolation, and prediction of failures and hazardous driving conditions as well as the associated level of confidence in the accuracy of the decisions. The performance of the safety device and operation under experimental conditions and constraints will be evaluated using end-to-end simulation and a testbed._x000D_
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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.

* Information listed above is at the time of submission. *

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