Award Abstract # 2140306
I-Corps: Advanced Truck Detection with Lidar Technology

NSF Org: TI
Translational Impacts
Recipient: UNIVERSITY OF ARKANSAS
Initial Amendment Date: August 4, 2021
Latest Amendment Date: August 4, 2021
Award Number: 2140306
Award Instrument: Standard Grant
Program Manager: Ruth Shuman
rshuman@nsf.gov
 (703)292-2160
TI
 Translational Impacts
TIP
 Directorate for Technology, Innovation, and Partnerships
Start Date: August 1, 2021
End Date: July 31, 2022 (Estimated)
Total Intended Award Amount: $50,000.00
Total Awarded Amount to Date: $50,000.00
Funds Obligated to Date: FY 2021 = $50,000.00
History of Investigator:
  • Sarah Hernandez (Principal Investigator)
    sarahvh@uark.edu
  • Jackson Cothren (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Arkansas
1125 W MAPLE ST STE 316
FAYETTEVILLE
AR  US  72701-3124
(479)575-3845
Sponsor Congressional District: 03
Primary Place of Performance: University of Arkansas
1125 W. Maple Street
Fayetteville
AR  US  72702-3124
Primary Place of Performance
Congressional District:
03
Unique Entity Identifier (UEI): MECEHTM8DB17
Parent UEI:
NSF Program(s): I-Corps
Primary Program Source: 01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 8083, 9150
Program Element Code(s): 802300
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.084

ABSTRACT

The broader impact/commercial potential of this I-Corps project is to enable transportation agencies to gather critical freight movement data using passively collected and anonymous sensors. Anonymity is of key importance for the successful collection of transportation datasets, especially in the competitive freight industry. Traditional approaches such as image-based detection, cell phone tracking, or other visual monitoring (license plate tracking or logo recognition) can violate privacy considerations and hinder widespread freight data collection. Such data collection is necessary for travel demand modeling and forecasting as well as for infrastructure planning, operations, and maintenance for roadways, bridges, and freight terminals. The market for this advanced truck detection device includes public transportation agencies at the city, county, state, and national levels, traffic sensing device manufacturers, transportation consulting companies, and freight terminal operators. While current sensors may distinguish trucks from cars or trucks by axle configuration, there are no non-pavement intrusive technologies currently able to predict the body-type of the vehicle in enough detail to indicate freight carried. Agencies tasked with data collection often must rely on time-consuming periodic surveys to estimate where and what freight is moving on their highway system, making it difficult to produce timely project cost-benefit and resilience/impact analyses. Commercial applications can be extended to large distribution centers, mining areas, rail yards or other intermodal terminals and ports.

This I-Corps project will further develop a system for low-cost, anonymous, and pavement-nonintrusive advanced truck detection by developing a side-fire (perpendicular to traffic flow) Lidar (Light Detection and Ranging)-based traffic detection and classification system. In side-fire configuration, Lidar sensors capture the profile of the truck (tractor and trailer/semi-trailer) which can be classified by body type with high-resolution while maintaining the anonymity of the driver, license/registration, and company. The innovation of this technology includes: 1) novel configuration and application of off-the-shelf Lidar technology for traffic detection, 2) coupling of technology with machine learning algorithms for feature detection, extraction, and classification with the aim of high-resolution truck classification, and 3) implementation of classification outputs in a data dashboard for real time and historical review. This novel truck detection solution using Lidar can enable a fundamental shift in how freight data is collected, especially by public transportation agencies.

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.

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.

The key outcome of this National Science Foundation?s (NSF) I-CORPS Program project was the development of a business model canvas for NOTION, a non-intrusive Lidar sensor for advanced traffic detection.  Through a series of 100 interviews, the project team developed and refined a business thesis that included nine validated hypothesis related to customer segments, key partners, revenue streams, and value propositions, among other aspects.  The final business model thesis for the product is ?Transportation Planners who have existing sensor infrastructure will use NOTION sensors because they will save $100k-$200k in data acquisition costs while supplying critical freight movement data not available from other products.  The project team consisted of a graduate student and associate professor from civil engineering, a professor from geosciences, and a team mentor. 

During the interview process and weekly meetings with cohort mentors and participants, the project team formulated and validated nine business model hypotheses.  Of these nine, the largest shift in our initial hypothesis and the final validated business model were for (1) customer segments and (2) value propositions. 

Initially the team identified engineers within data collection divisions at state Departments of Transportation (DOTs), transportation consultants, and traffic device manufacturers as customer segments.  After the interview process, the team determined that within the state DOT planners were the end users and recommenders of the product, while traffic engineers served as buyers, and maintenance staff played a significant role as key partners.  Overall transportation consultants and traffic data collection companies were not customer segments, but rather key partners. And lastly, traffic device manufacturers were possible distributors and key partners, but not part of the customer segment. This led to the formation of our beachhead market- planning division managers at the state department of transportation (DOT), specifically, DOTs with key freight generators such as ports, intermodal terminals, and/or border crossings.

When speaking with potential customers, the team was able to refine value propositions for NOTION.  The key value proposition centered on cost savings that state DOT planning divisions could apply to data acquisitions while collecting data that is not available from existing sensors.  Additionally, customer interviews revealed the importance of having an end-user interface where they can analyze and view the data since raw data streamed directly from the sensor is hard for them to handle and interpret. As a result, the team pivoted on the idea of selling just hardware to now include the idea of designing the device to be ?plug-and-play", complete with hardware and complementary software.  The team also explored, as per the suggested practices garnered from interviews with traffic device manufacturers, the idea of Software as a Service (SaaS) revenue streams.

After 100 interviews, individual mentoring from the cohort teaching team, and advice from the project mentor, the recommended future direction of NOTION sensor commercialization is to seek research and development grants to further technological development of the current NOTION prototype and to evaluate the potential for patenting the product.

 


Last Modified: 09/01/2022
Modified by: Sarah Hernandez

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