Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle Assessment
This study aims to generate test cases for scenario-based assessment of automated driving systems (ADS) when encounter a cut-out maneuver where the lead vehicle having changed lanes, revealing a new lead vehicle that, in some cases, is slower than the original lead (the cutting-out) vehicle. We extr...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10105956/ |
| _version_ | 1797828268513034240 |
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| author | H. Muslim S. Endo H. Imanaga S. Kitajima N. Uchida E. Kitahara K. Ozawa H. Sato H. Nakamura |
| author_facet | H. Muslim S. Endo H. Imanaga S. Kitajima N. Uchida E. Kitahara K. Ozawa H. Sato H. Nakamura |
| author_sort | H. Muslim |
| collection | DOAJ |
| description | This study aims to generate test cases for scenario-based assessment of automated driving systems (ADS) when encounter a cut-out maneuver where the lead vehicle having changed lanes, revealing a new lead vehicle that, in some cases, is slower than the original lead (the cutting-out) vehicle. We extracted the cut-out scenarios from an established real-world traffic dataset recorded by instrumented vehicles on Japanese highways and then defined them using vehicle kinematic parameters (velocities and distances). The extracted scenarios were analyzed based on the direct correlation between every two consecutive vehicles: a rear part that describes the correlation between the following vehicle and the cutting-out vehicle; and a frontal part that describes the correlation between the cutting-out vehicle and the preceding vehicle. Parameter ranges were quantified with a regression model and determined based on the risk acceptance threshold applied in the field of Japanese high-speed trains and annual exposure by professional highway drivers to produce a scenario space with a reasonably foreseeable range in which ADS may not produce crashes lest it performs worse than human drivers. A multi-dimensional distribution analytical approach was used to derive a correlation between the following and preceding vehicles considering the initial longitudinal velocities. Results suggest that when the time headway between the following vehicle and the cutting-out vehicle is equal to or more than 2 s, there should not have collision risks between the following vehicle and the preceding vehicle. These findings can help to understand normative driver behavior during cut-out scenarios and to generate accident-free scenario space for which ADS must perform flawlessly. |
| first_indexed | 2024-04-09T13:01:27Z |
| format | Article |
| id | doaj.art-2cff9a42313e420e9bc3c6dd9cece54b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T13:01:27Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2cff9a42313e420e9bc3c6dd9cece54b2023-05-12T23:00:20ZengIEEEIEEE Access2169-35362023-01-0111453494536310.1109/ACCESS.2023.326870310105956Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle AssessmentH. Muslim0https://orcid.org/0000-0001-6269-2055S. Endo1H. Imanaga2S. Kitajima3N. Uchida4https://orcid.org/0000-0003-2491-0140E. Kitahara5K. Ozawa6https://orcid.org/0000-0003-2258-0483H. Sato7H. Nakamura8Japan Automobile Research Institute, Tsukuba, JapanJapan Automobile Research Institute, Tsukuba, JapanJapan Automobile Research Institute, Tsukuba, JapanJapan Automobile Research Institute, Tsukuba, JapanJapan Automobile Research Institute, Tsukuba, JapanSafety Assurance KUdos for Reliable Autonomous Vehicles (SAKURA) Project, Tsukuba, JapanSafety Assurance KUdos for Reliable Autonomous Vehicles (SAKURA) Project, Tsukuba, JapanSafety Assurance KUdos for Reliable Autonomous Vehicles (SAKURA) Project, Tsukuba, JapanJapan Automobile Research Institute, Tsukuba, JapanThis study aims to generate test cases for scenario-based assessment of automated driving systems (ADS) when encounter a cut-out maneuver where the lead vehicle having changed lanes, revealing a new lead vehicle that, in some cases, is slower than the original lead (the cutting-out) vehicle. We extracted the cut-out scenarios from an established real-world traffic dataset recorded by instrumented vehicles on Japanese highways and then defined them using vehicle kinematic parameters (velocities and distances). The extracted scenarios were analyzed based on the direct correlation between every two consecutive vehicles: a rear part that describes the correlation between the following vehicle and the cutting-out vehicle; and a frontal part that describes the correlation between the cutting-out vehicle and the preceding vehicle. Parameter ranges were quantified with a regression model and determined based on the risk acceptance threshold applied in the field of Japanese high-speed trains and annual exposure by professional highway drivers to produce a scenario space with a reasonably foreseeable range in which ADS may not produce crashes lest it performs worse than human drivers. A multi-dimensional distribution analytical approach was used to derive a correlation between the following and preceding vehicles considering the initial longitudinal velocities. Results suggest that when the time headway between the following vehicle and the cutting-out vehicle is equal to or more than 2 s, there should not have collision risks between the following vehicle and the preceding vehicle. These findings can help to understand normative driver behavior during cut-out scenarios and to generate accident-free scenario space for which ADS must perform flawlessly.https://ieeexplore.ieee.org/document/10105956/Connected and automated vehiclescar-followinglane changelogical scenariossafety-test assessmentscenario-based approach |
| spellingShingle | H. Muslim S. Endo H. Imanaga S. Kitajima N. Uchida E. Kitahara K. Ozawa H. Sato H. Nakamura Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle Assessment IEEE Access Connected and automated vehicles car-following lane change logical scenarios safety-test assessment scenario-based approach |
| title | Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle Assessment |
| title_full | Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle Assessment |
| title_fullStr | Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle Assessment |
| title_full_unstemmed | Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle Assessment |
| title_short | Cut-Out Scenario Generation With Reasonability Foreseeable Parameter Range From Real Highway Dataset for Autonomous Vehicle Assessment |
| title_sort | cut out scenario generation with reasonability foreseeable parameter range from real highway dataset for autonomous vehicle assessment |
| topic | Connected and automated vehicles car-following lane change logical scenarios safety-test assessment scenario-based approach |
| url | https://ieeexplore.ieee.org/document/10105956/ |
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