Connected and Automated Vehicle (CAV) Testing Scenario Design and Implementation Using Naturalistic Driving Data and Augmented Reality
dc.contributor.author | Feng, Yiheng, Ph.D. | en_US |
dc.contributor.author | Bao, Shan, Ph.D. | en_US |
dc.contributor.author | Liu, Henry, Ph.D. | en_US |
dc.date.accessioned | 2023-03-15T19:04:56Z | |
dc.date.issued | 2023-03-15 | |
dc.identifier | UMTRI-2023-6 | en_US |
dc.identifier.citation | Feng, Y., Bao, S., & Liu, H.X. (2019). Connected and Automated Vehicle (CAV) Testing Scenario Design and Implementation Using Naturalistic Driving Data and Augmented Reality. Final Report. UMTRI-2023-6. | en_US |
dc.identifier.other | CCAT Final Report 7 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/175983 | |
dc.description | Final Report | en_US |
dc.description.abstract | Testing and evaluation is a critical step in the development and deployment of connected and automated vehicle (CAV) technology. Testing standards for human-driven vehicles, such as Federal Motor Vehicle Safety Standards (FMVSS), were established a long time ago. However, current standards cannot be applied to CAVs, because they often assume the presence of a human driver, who conducts the driving tasks. It is very important to develop test procedures and identify applicable test scenarios (user cases) for CAVs to evaluate the “intelligence” of the vehicle. The intelligence level indicates whether a CAV can drive safely and efficiently without human intervention. The newly released Automated Driving Systems Guideline 2 has made it very clear that the new automated driving systems need validation methods and to be tested by incorporating behavior competencies. In this research, a unified framework is designed to solve the entire test scenario library generation (TSLG) problem, where a novel method is proposed for the library generation question. Theoretical analysis provides justifications of the proposed method regarding both evaluation accuracy and efficiency. Specifically, the proposed method obtains unbiased index estimation of performance metrics (i.e., accuracy) with a fewer number of required tests (i.e., efficiency). The three case studies verify the proposed methodology and the results show that the evaluation process can be accelerated by 103 times compared with the NDD evaluation method, with the same accuracy. | en_US |
dc.description.sponsorship | U.S. Department of Transportation Office of the Assistant Secretary for Research and Technology | en_US |
dc.format.extent | 33 | en_US |
dc.language | English | en_US |
dc.publisher | University of Michigan Transportation Research Institute | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject.other | connected vehicles | en_US |
dc.subject.other | automated vehicles | en_US |
dc.subject.other | augmented reality | en_US |
dc.subject.other | acceptance testing and evaluation | en_US |
dc.title | Connected and Automated Vehicle (CAV) Testing Scenario Design and Implementation Using Naturalistic Driving Data and Augmented Reality | en_US |
dc.type | Technical Report | |
dc.subject.hlbsecondlevel | Transportation | |
dc.subject.hlbtoplevel | Engineering | |
dc.contributor.affiliationum | University of Michigan Transportation Research Institute | |
dc.contributor.affiliationumcampus | Ann Arbor | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/175983/1/CAV Testing Scenario Design and Implementation Using Naturalistic Driving Data and Augmented Reality Final Report.pdf | |
dc.identifier.doi | https://dx.doi.org/10.7302/7023 | |
dc.identifier.orcid | https://orcid.org/0000-0001-5656-3222 | en_US |
dc.identifier.orcid | https://orcid.org/0000-0002-3685-9920 | en_US |
dc.identifier.orcid | https://orcid.org/0000-0002-3685-9920 | en_US |
dc.description.filedescription | Description of CAV Testing Scenario Design and Implementation Using Naturalistic Driving Data and Augmented Reality Final Report.pdf : Final Report | |
dc.identifier.name-orcid | Feng, Yiheng; 0000-0001-5656-3222 | en_US |
dc.identifier.name-orcid | Liu, Henry; 0000-0002-3685-9920 | en_US |
dc.working.doi | 10.7302/7023 | en_US |
dc.owningcollname | Transportation Research Institute (UMTRI) |
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