A Methodology for Gauging Usage Opportunities for Partially Automated Vehicles with Application to Public Travel Survey Data Sets


Kenneth P. Laberteaux (TRI-NA), Karim Hamza (TRI-NA), Alan M. Berger, Casey L. Brown


Vehicle automation has garnered a significant amount of interest in the recent years. When assessing automated driving (ad) capability of a vehicle, it is important to distinguish between full automation, in which no human driver is required, and partial automation, where a human driver may be required to occasionally intervene and/or take control of the vehicle for portions of the trip. This paper presents a methodology for assessing usage opportunities of partial ad in light-duty vehicle fleets. Key assumptions are: 1) the longer the time fraction of driving where ad is active, the better, and 2) drivers will value having longer contiguous sections of ad-active time over having to frequently regain vehicle control. Given second-by-second records of real-world driving trips, the methodology uses a fuzzy inference system to estimate the fraction of driving time at a certain “quality of use” level. Performing the quality of use assessment for all trips/ vehicles in a representative data set can then provide insight to the fraction of population that would likely find partial ad desirable. To demonstrate the proposed methodology, data on vehicle trips from public travel surveys in California (chts) and Atlanta (arcts) are used along with simplified prototypical models for partial ad. Simulation results are generally in agreement with common perceptions, but show a wide range of possibilities, which could be further narrowed down when more detailed trip data becomes available.


Transportation Research Board 2017 annual meeting



2017 Transportation Research Record Series (TRR-Journal of the Transportation Research Board (double peer reviewed by standing committee on vehicle-highway automation -ahb30)

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