An autonomous vehicles’ test case extraction method: Example of vehicle-to-pedestrian scenarios
Testing autonomous vehicles (AVs) in hazardous scenarios is a crucial technical approach to ensure their safety. A key aspect of this process is the generation of hazard scenarios. In general, such scenarios are generated through cluster analysis of traffic accident data. However, this approach may...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024171046 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850278606300774400 |
|---|---|
| author | Zhengping Tan Qian Wang Wenhao Hu Pingfei Li Liangliang Shi Hao Feng |
| author_facet | Zhengping Tan Qian Wang Wenhao Hu Pingfei Li Liangliang Shi Hao Feng |
| author_sort | Zhengping Tan |
| collection | DOAJ |
| description | Testing autonomous vehicles (AVs) in hazardous scenarios is a crucial technical approach to ensure their safety. A key aspect of this process is the generation of hazard scenarios. In general, such scenarios are generated through cluster analysis of traffic accident data. However, this approach may not fully capture the criticality of the generated scenarios, as it tends to emphasize the statistical characteristics of the data rather than its real-world applicability. This paper proposes a novel method to enhance scenario adaptation by integrating quantization weights with a new clustering algorithm. These weights, representing the correlation between scenario elements and the AV system, are calculated using fuzzy comprehensive evaluation (FCE). The proposed method is applied to 1044 pedestrian accident cases in China, resulting in the identification of nine categories of typical scenarios and corresponding test schemes for both the perception and decision-making systems of AVs. The results show that the new method increases the proportion of critical scenarios by 17.4 % and 13.6 %, respectively, compared to traditional methods. Overall, the critical scenarios generated in this paper can significantly improve the testing efficiency and safety of AVs. |
| format | Article |
| id | doaj-art-5cfb052d4ea14a16ac52e3eab324a7e8 |
| institution | OA Journals |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-5cfb052d4ea14a16ac52e3eab324a7e82025-08-20T01:49:26ZengElsevierHeliyon2405-84402025-01-01111e4107310.1016/j.heliyon.2024.e41073An autonomous vehicles’ test case extraction method: Example of vehicle-to-pedestrian scenariosZhengping Tan0Qian Wang1Wenhao Hu2Pingfei Li3Liangliang Shi4Hao Feng5School of Automobile &Transportation, Xihua University, Chengdu, 610039, China; Sichuan Xihua Jiaotong Forensic Science Center, Chengdu, 610039, China; Corresponding author. School of Automobile &Transportation, Xihua University, Chengdu, 610039, China; Sichuan Xihua Jiaotong Forensic Science Center, Chengdu, 610039, China.School of Automobile &Transportation, Xihua University, Chengdu, 610039, ChinaState Administration for Market Regulation Defective Product Recall Technical Center (DPRC), Beijing, 100101, ChinaSchool of Automobile &Transportation, Xihua University, Chengdu, 610039, China; Sichuan Xihua Jiaotong Forensic Science Center, Chengdu, 610039, ChinaChina Automotive Engineering Research Institute, Chongqing, 401122, ChinaKey Lab of Forensic Science, Ministry of Justice, China (Academy of Forensic Science), Shanghai, 200063, China; Corresponding author. Key Lab of Forensic Science, Ministry of Justice, China (Academy of Forensic Science), Shanghai, 200063, China.Testing autonomous vehicles (AVs) in hazardous scenarios is a crucial technical approach to ensure their safety. A key aspect of this process is the generation of hazard scenarios. In general, such scenarios are generated through cluster analysis of traffic accident data. However, this approach may not fully capture the criticality of the generated scenarios, as it tends to emphasize the statistical characteristics of the data rather than its real-world applicability. This paper proposes a novel method to enhance scenario adaptation by integrating quantization weights with a new clustering algorithm. These weights, representing the correlation between scenario elements and the AV system, are calculated using fuzzy comprehensive evaluation (FCE). The proposed method is applied to 1044 pedestrian accident cases in China, resulting in the identification of nine categories of typical scenarios and corresponding test schemes for both the perception and decision-making systems of AVs. The results show that the new method increases the proportion of critical scenarios by 17.4 % and 13.6 %, respectively, compared to traditional methods. Overall, the critical scenarios generated in this paper can significantly improve the testing efficiency and safety of AVs.http://www.sciencedirect.com/science/article/pii/S2405844024171046Autonomous vehiclesTest scenarioFuzzy comprehensive evaluationPerception systemDecision-making systemClustering algorithm |
| spellingShingle | Zhengping Tan Qian Wang Wenhao Hu Pingfei Li Liangliang Shi Hao Feng An autonomous vehicles’ test case extraction method: Example of vehicle-to-pedestrian scenarios Heliyon Autonomous vehicles Test scenario Fuzzy comprehensive evaluation Perception system Decision-making system Clustering algorithm |
| title | An autonomous vehicles’ test case extraction method: Example of vehicle-to-pedestrian scenarios |
| title_full | An autonomous vehicles’ test case extraction method: Example of vehicle-to-pedestrian scenarios |
| title_fullStr | An autonomous vehicles’ test case extraction method: Example of vehicle-to-pedestrian scenarios |
| title_full_unstemmed | An autonomous vehicles’ test case extraction method: Example of vehicle-to-pedestrian scenarios |
| title_short | An autonomous vehicles’ test case extraction method: Example of vehicle-to-pedestrian scenarios |
| title_sort | autonomous vehicles test case extraction method example of vehicle to pedestrian scenarios |
| topic | Autonomous vehicles Test scenario Fuzzy comprehensive evaluation Perception system Decision-making system Clustering algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024171046 |
| work_keys_str_mv | AT zhengpingtan anautonomousvehiclestestcaseextractionmethodexampleofvehicletopedestrianscenarios AT qianwang anautonomousvehiclestestcaseextractionmethodexampleofvehicletopedestrianscenarios AT wenhaohu anautonomousvehiclestestcaseextractionmethodexampleofvehicletopedestrianscenarios AT pingfeili anautonomousvehiclestestcaseextractionmethodexampleofvehicletopedestrianscenarios AT liangliangshi anautonomousvehiclestestcaseextractionmethodexampleofvehicletopedestrianscenarios AT haofeng anautonomousvehiclestestcaseextractionmethodexampleofvehicletopedestrianscenarios AT zhengpingtan autonomousvehiclestestcaseextractionmethodexampleofvehicletopedestrianscenarios AT qianwang autonomousvehiclestestcaseextractionmethodexampleofvehicletopedestrianscenarios AT wenhaohu autonomousvehiclestestcaseextractionmethodexampleofvehicletopedestrianscenarios AT pingfeili autonomousvehiclestestcaseextractionmethodexampleofvehicletopedestrianscenarios AT liangliangshi autonomousvehiclestestcaseextractionmethodexampleofvehicletopedestrianscenarios AT haofeng autonomousvehiclestestcaseextractionmethodexampleofvehicletopedestrianscenarios |