Connected and Automated Vehicle (CAV) Testing Scenario Design and Implementation Using Naturalistic Driving Data and Augmented Reality
Feng, Yiheng, Ph.D.; Bao, Shan, Ph.D.; Liu, Henry, Ph.D.
CAV Testing Scenario Design and Implementation Using Naturalistic Driving Data and Augmented Reality Final Report.pdf
University of Michigan Transportation Research Institute
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.
AbstractTesting 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.
Deep Blue DOI
CCAT Final Report 7
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