Details
| Original language | English |
|---|---|
| Article number | 6750 |
| Journal | Applied Sciences (Switzerland) |
| Volume | 13 |
| Issue number | 11 |
| Publication status | Published - 1 Jun 2023 |
Abstract
Traffic accident avoidance and mitigation are the main targets of accident research and vehicle safety development worldwide. Despite improving advanced driver assistance systems (ADAS) and active safety systems, it will not be possible to avoid all vehicle accidents in the near future. Innovative Pre-Crash systems (PCS) should contribute to the accident mitigation of unavoidable accidents. However, there are no standardized testing methods for Pre-Crash systems. In particular, irreversible Pre-Crash systems lead to great challenges in the verification and validation (V&V) process. The reliable and precise real-time crash severity prediction (CSP) is, however, the basic prerequisite for irreversible PCS activation. This study proposes a novel validation and safety assessment strategy for a perception-based crash severity prediction function. In doing so, the intended functionality, safety and validation requirements of PCS are worked out in the context of ISO 26262 and ISO/PAS 21448 standards. In order to reduce the testing effort, a real-data-driven scenario-based testing approach is applied. Therefore, the authors present a novel unsupervised machine learning methodology for the creation of concrete and logical test scenario catalogs based on K-Means++ and k-NN algorithms. The developed methodology is used on the GIDAS database to extract 35 representative clusters of car to car collision scenarios, which are utilized for virtual testing. The limitations of the presented method are disclosed afterwards to help future research to set the right focus.
Keywords
- accident scenario clustering, crash severity prediction, functional safety, Pre-Crash systems, validation and verification
ASJC Scopus subject areas
- Materials Science(all)
- Physics and Astronomy(all)
- Instrumentation
- Engineering(all)
- Chemical Engineering(all)
- Process Chemistry and Technology
- Computer Science(all)
- Computer Science Applications
- Chemical Engineering(all)
- Fluid Flow and Transfer Processes
Sustainable Development Goals
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In: Applied Sciences (Switzerland), Vol. 13, No. 11, 6750, 01.06.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Predictive Vehicle Safety
T2 - Validation Strategy of a Perception-Based Crash Severity Prediction Function
AU - Putter, Roman
AU - Neubohn, Andre
AU - Leschke, Andre
AU - Lachmayer, Roland
N1 - Funding Information: This research project is supported by Volkswagen AG. The results, opinions and conclusions expressed in this publication are those of the authors and do not necessarily represent the views of Volkswagen AG.
PY - 2023/6/1
Y1 - 2023/6/1
N2 - Traffic accident avoidance and mitigation are the main targets of accident research and vehicle safety development worldwide. Despite improving advanced driver assistance systems (ADAS) and active safety systems, it will not be possible to avoid all vehicle accidents in the near future. Innovative Pre-Crash systems (PCS) should contribute to the accident mitigation of unavoidable accidents. However, there are no standardized testing methods for Pre-Crash systems. In particular, irreversible Pre-Crash systems lead to great challenges in the verification and validation (V&V) process. The reliable and precise real-time crash severity prediction (CSP) is, however, the basic prerequisite for irreversible PCS activation. This study proposes a novel validation and safety assessment strategy for a perception-based crash severity prediction function. In doing so, the intended functionality, safety and validation requirements of PCS are worked out in the context of ISO 26262 and ISO/PAS 21448 standards. In order to reduce the testing effort, a real-data-driven scenario-based testing approach is applied. Therefore, the authors present a novel unsupervised machine learning methodology for the creation of concrete and logical test scenario catalogs based on K-Means++ and k-NN algorithms. The developed methodology is used on the GIDAS database to extract 35 representative clusters of car to car collision scenarios, which are utilized for virtual testing. The limitations of the presented method are disclosed afterwards to help future research to set the right focus.
AB - Traffic accident avoidance and mitigation are the main targets of accident research and vehicle safety development worldwide. Despite improving advanced driver assistance systems (ADAS) and active safety systems, it will not be possible to avoid all vehicle accidents in the near future. Innovative Pre-Crash systems (PCS) should contribute to the accident mitigation of unavoidable accidents. However, there are no standardized testing methods for Pre-Crash systems. In particular, irreversible Pre-Crash systems lead to great challenges in the verification and validation (V&V) process. The reliable and precise real-time crash severity prediction (CSP) is, however, the basic prerequisite for irreversible PCS activation. This study proposes a novel validation and safety assessment strategy for a perception-based crash severity prediction function. In doing so, the intended functionality, safety and validation requirements of PCS are worked out in the context of ISO 26262 and ISO/PAS 21448 standards. In order to reduce the testing effort, a real-data-driven scenario-based testing approach is applied. Therefore, the authors present a novel unsupervised machine learning methodology for the creation of concrete and logical test scenario catalogs based on K-Means++ and k-NN algorithms. The developed methodology is used on the GIDAS database to extract 35 representative clusters of car to car collision scenarios, which are utilized for virtual testing. The limitations of the presented method are disclosed afterwards to help future research to set the right focus.
KW - accident scenario clustering
KW - crash severity prediction
KW - functional safety
KW - Pre-Crash systems
KW - validation and verification
UR - http://www.scopus.com/inward/record.url?scp=85161508036&partnerID=8YFLogxK
U2 - 10.3390/app13116750
DO - 10.3390/app13116750
M3 - Article
AN - SCOPUS:85161508036
VL - 13
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
SN - 2076-3417
IS - 11
M1 - 6750
ER -