Collection and classification of influence parameters for safety effectiveness of ADAS
Virtual scenario-based testing has become an acceptable method for evaluating safety effectiveness of advanced driver assistance systems (ADAS). Due to the complexity of the ADAS operating environment, the scenarios that an ADAS could face are almost infinite. Therefore, it is crucial to find critic...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Future Transportation |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/ffutr.2023.945599/full |
| _version_ | 1797853384584200192 |
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| author | Fengwei Guo Anton Fuchs Stefan Kirschbichler Wolfgang Sinz Ernst Tomasch Hermann Steffan Joerg Moser |
| author_facet | Fengwei Guo Anton Fuchs Stefan Kirschbichler Wolfgang Sinz Ernst Tomasch Hermann Steffan Joerg Moser |
| author_sort | Fengwei Guo |
| collection | DOAJ |
| description | Virtual scenario-based testing has become an acceptable method for evaluating safety effectiveness of advanced driver assistance systems (ADAS). Due to the complexity of the ADAS operating environment, the scenarios that an ADAS could face are almost infinite. Therefore, it is crucial to find critical scenarios to improve the efficiency of testing without compromising credibility. One popular method is to explore the parameterized scenario space using various intelligent search methods. Selecting parameters to parameterize the scenario space is particularly important to achieve good coverage and high efficiency. However, an extensive collection of (relevant) influence parameters is missing, which allows a thorough consideration when selecting parameters regarding specific scenarios. In addition, the general importance definition for individual influence parameters is not provided, regarding the potential influence of their variations on the safety effectiveness of ADAS, which can also be used as a reference while selecting parameters. Combining knowledge from different sources (the published literature, standardized test scenarios, accident analysis, autonomous vehicle disengagement, accident reports, and specific online surveys), this paper has summarized, in total, 94 influence parameters, given the general definitions of importance for 77 influence parameters based on cluster analysis algorithms. The list of influence parameters provides researchers and system developers a comprehensive basis for pre-selecting influence parameters for evaluating the safety effectiveness of ADAS by virtual scenario-based testing and helps check whether certain influence parameters can be a meaningful extension for the evaluation. |
| first_indexed | 2024-04-09T19:49:48Z |
| format | Article |
| id | doaj.art-6a52800d1c83433694173c7dfddaf2a9 |
| institution | Directory Open Access Journal |
| issn | 2673-5210 |
| language | English |
| last_indexed | 2024-04-09T19:49:48Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Future Transportation |
| spelling | doaj.art-6a52800d1c83433694173c7dfddaf2a92023-04-03T10:04:20ZengFrontiers Media S.A.Frontiers in Future Transportation2673-52102023-04-01410.3389/ffutr.2023.945599945599Collection and classification of influence parameters for safety effectiveness of ADASFengwei Guo0Anton Fuchs1Stefan Kirschbichler2Wolfgang Sinz3Ernst Tomasch4Hermann Steffan5Joerg Moser6Vehicle Safety Institute, Graz University of Technology, Graz, AustriaVirtual Vehicle Research GmbH, Graz, AustriaVirtual Vehicle Research GmbH, Graz, AustriaVehicle Safety Institute, Graz University of Technology, Graz, AustriaVehicle Safety Institute, Graz University of Technology, Graz, AustriaVehicle Safety Institute, Graz University of Technology, Graz, AustriaVehicle Safety Institute, Graz University of Technology, Graz, AustriaVirtual scenario-based testing has become an acceptable method for evaluating safety effectiveness of advanced driver assistance systems (ADAS). Due to the complexity of the ADAS operating environment, the scenarios that an ADAS could face are almost infinite. Therefore, it is crucial to find critical scenarios to improve the efficiency of testing without compromising credibility. One popular method is to explore the parameterized scenario space using various intelligent search methods. Selecting parameters to parameterize the scenario space is particularly important to achieve good coverage and high efficiency. However, an extensive collection of (relevant) influence parameters is missing, which allows a thorough consideration when selecting parameters regarding specific scenarios. In addition, the general importance definition for individual influence parameters is not provided, regarding the potential influence of their variations on the safety effectiveness of ADAS, which can also be used as a reference while selecting parameters. Combining knowledge from different sources (the published literature, standardized test scenarios, accident analysis, autonomous vehicle disengagement, accident reports, and specific online surveys), this paper has summarized, in total, 94 influence parameters, given the general definitions of importance for 77 influence parameters based on cluster analysis algorithms. The list of influence parameters provides researchers and system developers a comprehensive basis for pre-selecting influence parameters for evaluating the safety effectiveness of ADAS by virtual scenario-based testing and helps check whether certain influence parameters can be a meaningful extension for the evaluation.https://www.frontiersin.org/articles/10.3389/ffutr.2023.945599/fulladvanced driver assistance systemsinfluence parametersscenario-based testingsafety effectivenesscluster analysis |
| spellingShingle | Fengwei Guo Anton Fuchs Stefan Kirschbichler Wolfgang Sinz Ernst Tomasch Hermann Steffan Joerg Moser Collection and classification of influence parameters for safety effectiveness of ADAS Frontiers in Future Transportation advanced driver assistance systems influence parameters scenario-based testing safety effectiveness cluster analysis |
| title | Collection and classification of influence parameters for safety effectiveness of ADAS |
| title_full | Collection and classification of influence parameters for safety effectiveness of ADAS |
| title_fullStr | Collection and classification of influence parameters for safety effectiveness of ADAS |
| title_full_unstemmed | Collection and classification of influence parameters for safety effectiveness of ADAS |
| title_short | Collection and classification of influence parameters for safety effectiveness of ADAS |
| title_sort | collection and classification of influence parameters for safety effectiveness of adas |
| topic | advanced driver assistance systems influence parameters scenario-based testing safety effectiveness cluster analysis |
| url | https://www.frontiersin.org/articles/10.3389/ffutr.2023.945599/full |
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