Bolstering Awareness with Real-Time Rider Alert and Cueing for Upcoming Danger Avoidance (BARRACUDA)

Award Information
Agency: Department of Transportation
Branch: N/A
Contract: DTRT5717C10036
Agency Tracking Number: 151FH3005II
Amount: $990,757.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: 151FH3
Solicitation Number: DTRT5715RSBIR1
Solicitation Year: 2015
Award Year: 2017
Award Start Date (Proposal Award Date): 2016-12-15
Award End Date (Contract End Date): 2018-12-14
Small Business Information
625 Mount Auburn Street, Cambridge, MA, 02138
DUNS: 115243701
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Michael Jenkins
 Senior Scientist
 (617) 491-3474
Business Contact
 Mark Felix
Title: Director, Contracts
Phone: (617) 491-3474
Research Institution
In addition to hazards relevant to automobiles, motorcycle riders must remain vigilant for hazards that pose significant danger uniquely to motorcycles, such as uneven terrain, sand/gravel, and potholes. However, there is currently no motorcycle-specific hazard tracking or alerting system available for riders. To address this need, Charles River Analytics executed a successful Phase I effort to design and demonstrate the feasibility of a system for Bolstering Awareness with Real-Time Rider Alerting and Cueing for Upcoming Danger Avoidance (BARRACUDA). Our Phase I prototype captures and integrates relevant information from an array of public databases and a custom crowdsourced hazard reporting capability. It then fuses this information and applies automated geolocalization and risk assessment algorithms to allow riders to plan safer routes prior to departure (via a route planning smartphone application), and be alerted to upcoming hazards while en route (via an augmented reality heads-up display combined with a proof-of-concept ecologically-grounded display symbology). In Phase II, we propose to design, develop, and evaluate a full-scope BARRACUDA system, primarily through refinement of current capabilities, development of computer vision-based hazard detection capabilities, integration of additional alerting modalities, and validation through human-use studies.

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

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