Abstract
Automated driving systems (ADS) are envisioned to operate in a complex open world. To ensure that difficult environmental conditions do not trigger inadequate system behavior, it is crucial to identify difficult conditions and test whether they expose specific functional insufficiencies. The standard ISO 21448 introduces the concept of Triggering Conditions but does not provide designated methods for finding such conditions. Thus, we present an analytical method to systematically identify triggering conditions. The method supports domain experts to comprehensively model given driving scenarios and exhaustively deduce potential hazardous behaviors of the ADS. We show the method’s applicability by conducting a workshop series with 16 domain experts. There, 122 triggering conditions covering the sense-plan-act chain of ADS are elicited, based on which eight clusters are further identified.
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Notes
- 1.
Full catalog of triggering conditions: https://doi.org/10.5281/zenodo.11472687.
References
Adee, A., Gansch, R., Liggesmeyer, P., Glaeser, C., Drews, F.: Discovery of perception performance limiting triggering conditions in automated driving. In: 5th International Conference on System Reliablity and Safety (2021)
Althoff, M., Koschi, M., Manzinger, S.: CommonRoad: composable benchmarks for motion planning on roads. In: IEEE Intelligent Vehicles Symposium (2017)
Amersbach, C.T.: Functional decomposition approach-reducing the safety validation effort for highly automated driving. Ph.D. thesis, TU Darmstadt (2020)
Breitenstein, J., Termohlen, J.A., Lipinski, D., Fingscheidt, T.: Systematization of corner cases for visual perception in automated driving. In: IEEE Intelligent Vehicles Symposium (2020)
Crawley, F., Tyler, B.: HAZOP: Guide to Best Practice. Elsevier, Amsterdam (2015)
Hartjen, L.: Semantic Classification of Urban Traffic Scenarios for the Validation of Automated Driving Systems. Ph.D. thesis, TU Braunschweig (2023)
IEC 60812:2018: Failure modes and effects analysis (FMEA and FMECA). Standard, TC 56 - Dependability, Geneva, Switzerland (2018)
IEC 61025:2006: Fault tree analysis (FTA). Standard, TC 56 - Dependability, Geneva, Switzerland (2006)
ISO 21448:2022: Road vehicles - Safety of the intended functionality. Standard, Geneva, Switzerland (2022)
Khatun, M., Glaß, M., Jung, R.: Scenario-based extended hara incorporating functional safety & sotif for autonomous driving. In: ESREL - 30th European Safety and Reliability Conference (2020)
Kramer, B., Neurohr, C., Büker, M., Böde, E., Fränzle, M., Damm, W.: Identification and quantification of hazardous scenarios for automated driving. In: Zeller, M., Höfig, K. (eds.) IMBSA 2020. LNCS, vol. 12297, pp. 163–178. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58920-2_11
Leveson, N., Thomas, J.: STPA handbook (2018)
Linnhoff, C., Rosenberger, P., Schmidt, S., Elster, L., Stark, R., Winner, H.: Towards serious perception sensor simulation for safety validation of automated driving - a collaborative method to specify sensor models. In: IEEE Transportation on Intelligent Transport System (2021)
Martin, H., Winkler, B., Grubmüller, S., Watzenig, D.: Identification of performance limitations of sensing technologies for automated driving. In: IEEE International Conference on Connected Vehicles and Expo (2019)
Peng, L., Wang, H., Li, J.: Uncertainty evaluation of object detection algorithms for autonomous vehicles. Autom. Innov. 4, 241–252 (2021)
Philipp, R., et al.: Systematization of relevant road users for the evaluation of autonomous vehicle perception. In: IEEE International System Conference (2022)
Ren, L., Yin, H., Ge, W., Meng, Q.: Environment influences on uncertainty of object detection for automated driving systems. In: International Congress on Image and Signal Processing, BioMedical Engineering and Informatics. IEEE (2019)
Scholtes, M., et al.: 6-layer model for a structured description and categorization of urban traffic and environment. IEEE Access 9, 59131–59147 (2021)
Sharma, O., Sahoo, N., Puhan, N.: Recent advances in motion and behavior planning techniques for software architecture of autonomous vehicles: a state-of-the-art survey. Eng. Appl. Artif. Intell. 101, 104211 (2021)
Tong, J., Xing, X., Guo, R., Jiang, W., Xiong, L., Chen, J.: Performance limitations analysis of visual sensors in low light conditions based on field test. Technical report, SAE Technical Paper (2022)
UL 4600: Standard for evaluation of autonomous products, 1 edn. Standard, UL (2020)
Ulbrich, S., Menzel, T., Reschka, A., Schuldt, F., Maurer, M.: Defining and substantiating the terms scene, situation, and scenario for automated driving. In: Proceedings of IEEE International Conference on Intelligent Transportation System (2015)
Xing, X., Jia, T., Chen, J., Xiong, L., Yu, Z.: An ontology-based method to identify triggering conditions for perception insufficiency of autonomous vehicles. arXiv preprint arXiv:2210.08724 (2022)
Zhang, X., et al.: Finding critical scenarios for automated driving systems: a systematic mapping study. IEEE Trans. Softw. Eng. 49, 991–1026 (2023)
Zhu, Z., Philipp, R., Hungar, C., Howar, F.: Systematization and identification of triggering conditions: a preliminary step for efficient testing of autonomous vehicles. In: IEEE Intelligent Vehicles Symposium (2022)
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Zhu, Z., Philipp, R., Hungar, C., Howar, F. (2024). Identifying Difficult Environmental Conditions with Scenario-Based Hazard and Fault Analysis. In: Ceccarelli, A., Trapp, M., Bondavalli, A., Schoitsch, E., Gallina, B., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops. SAFECOMP 2024. Lecture Notes in Computer Science, vol 14989. Springer, Cham. https://doi.org/10.1007/978-3-031-68738-9_10
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