Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality Thresholds
The safety approval and assessment of automated driving systems (ADS) are becoming sophisticated and challenging tasks. Because the number of traffic scenarios is vast, it is essential to assess their criticality and extract the ones that present a safety risk. In this paper, we are proposing a nove...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/22/8780 |
| _version_ | 1797463981073367040 |
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| author | Demin Nalic Tomislav Mihalj Faris Orucevic Martin Schabauer Cornelia Lex Wolfgang Sinz Arno Eichberger |
| author_facet | Demin Nalic Tomislav Mihalj Faris Orucevic Martin Schabauer Cornelia Lex Wolfgang Sinz Arno Eichberger |
| author_sort | Demin Nalic |
| collection | DOAJ |
| description | The safety approval and assessment of automated driving systems (ADS) are becoming sophisticated and challenging tasks. Because the number of traffic scenarios is vast, it is essential to assess their criticality and extract the ones that present a safety risk. In this paper, we are proposing a novel method based on the time-to-react (TTR) measurement, which has advantages in considering avoidance possibilities. The method incorporates the concept of fictive vehicles and variable criticality thresholds (VCTs) to assess the overall scenario’s criticality. By introducing variable thresholds, a criticality scale is defined and used for criticality calculation. Based on this scale, the presented method determines the criticality of the lanes adjacent to the ego vehicle. This is performed by placing fictive vehicles in the adjacent lanes, which represent copies of the ego. The effectiveness of the method is demonstrated in two highway scenarios, with and without trailing vehicles. Results show different criticality for the two scenarios. The overall criticality of the scenario with trailing vehicles is higher due to the decrease in avoidance possibilities for the ego vehicle. |
| first_indexed | 2024-03-09T18:01:25Z |
| format | Article |
| id | doaj.art-c807133692a7455787eb49e32c1dbec8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:01:25Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c807133692a7455787eb49e32c1dbec82023-11-24T09:55:39ZengMDPI AGSensors1424-82202022-11-012222878010.3390/s22228780Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality ThresholdsDemin Nalic0Tomislav Mihalj1Faris Orucevic2Martin Schabauer3Cornelia Lex4Wolfgang Sinz5Arno Eichberger6ADAS Department, MQS Automotive AT OG, 8074 Raaba, AustriaInstitute of Automotive Engineering, Graz University of Technology, 8010 Graz, AustriaInstitute of Automotive Engineering, Graz University of Technology, 8010 Graz, AustriaInstitute of Automotive Engineering, Graz University of Technology, 8010 Graz, AustriaInstitute of Automotive Engineering, Graz University of Technology, 8010 Graz, AustriaADAS Simulation and Data Analysis Department, MAGNA Steyr Fahrzeugtechnik AG Co. & KG, 8041 Graz, AustriaInstitute of Automotive Engineering, Graz University of Technology, 8010 Graz, AustriaThe safety approval and assessment of automated driving systems (ADS) are becoming sophisticated and challenging tasks. Because the number of traffic scenarios is vast, it is essential to assess their criticality and extract the ones that present a safety risk. In this paper, we are proposing a novel method based on the time-to-react (TTR) measurement, which has advantages in considering avoidance possibilities. The method incorporates the concept of fictive vehicles and variable criticality thresholds (VCTs) to assess the overall scenario’s criticality. By introducing variable thresholds, a criticality scale is defined and used for criticality calculation. Based on this scale, the presented method determines the criticality of the lanes adjacent to the ego vehicle. This is performed by placing fictive vehicles in the adjacent lanes, which represent copies of the ego. The effectiveness of the method is demonstrated in two highway scenarios, with and without trailing vehicles. Results show different criticality for the two scenarios. The overall criticality of the scenario with trailing vehicles is higher due to the decrease in avoidance possibilities for the ego vehicle.https://www.mdpi.com/1424-8220/22/22/8780fictive vehiclessafety assessmentscenario criticalityautomated driving |
| spellingShingle | Demin Nalic Tomislav Mihalj Faris Orucevic Martin Schabauer Cornelia Lex Wolfgang Sinz Arno Eichberger Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality Thresholds Sensors fictive vehicles safety assessment scenario criticality automated driving |
| title | Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality Thresholds |
| title_full | Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality Thresholds |
| title_fullStr | Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality Thresholds |
| title_full_unstemmed | Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality Thresholds |
| title_short | Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality Thresholds |
| title_sort | criticality assessment method for automated driving systems by introducing fictive vehicles and variable criticality thresholds |
| topic | fictive vehicles safety assessment scenario criticality automated driving |
| url | https://www.mdpi.com/1424-8220/22/22/8780 |
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