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Development of Test Cases for Automated Vehicle Driving Safety Assessment Using Driving Trajectoriesoa mark
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Publication Year
2024-12-01
Journal
Sensors
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Citation
Sensors, Vol.24 No.24
Keyword
assessmentautomated vehiclesdriving safetytest casestrajectory
Mesh Keyword
AssessmentAutomated vehiclesCase basedDriving safetyFunctionalsLane change trajectoriesSafety assessmentsScenario-basedTest caseVideo data analysis
All Science Classification Codes (ASJC)
Analytical ChemistryInformation SystemsAtomic and Molecular Physics, and OpticsBiochemistryInstrumentationElectrical and Electronic Engineering
Abstract
For consumers to have confidence in the safety of automated vehicles (AVs), AVs must be assessed using systematically developed scenarios to verify driving safety and reliability. In particular, verification using scenarios has been widely performed for the assessment and certification of AVs. This study aims to develop test cases based on driving trajectories to assess the driving safety of AVs. To achieve this, concrete scenarios were systematically developed from functional and logical scenarios. Drone video data analysis was conducted to extract representative lane-change trajectories for AVs on expressway ramp sections. Subsequently, the test cases were selected from concrete scenarios through simulations using time-to-steer (TTS). Finally, the effectiveness of utilizing trajectories for scenario-based driving safety assessments was verified. Furthermore, it is expected that this approach can be applied to other driving patterns by providing a detailed procedure for the test case developed in this study.
ISSN
1424-8220
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/38094
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85213218469&origin=inward
DOI
https://doi.org/10.3390/s24247981
Journal URL
http://www.mdpi.com/journal/sensors
Type
Article
Funding
This study was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure, and Transport (Grant No. RS-2021-KA160637).
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Lee, Soo Mok Image
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Department of Mobility Engineering
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