Scenario Reduction of ALKS Development by Using Searching Methods

Year 2024, Volume: 1 Issue: 1, 59 - 69, 20.07.2024

Namık Zengin Oğuzhan Derebaşı Serdar Kınay Bekir Öztürk Harun Kutucu

Abstract

This study presents an approach in Automated Lane Keeping Systems (ALKS) within Automated Driving Systems (ADS), integrating scenario parameterization with Particle Swarm Optimization (PSO) and contrasting it with combinatorial testing (CT). Focusing on critical scenarios vital for ALKS safety, the approach uses UN Regulation 157 to establish a parameter space mirroring real-world driving conditions, ensuring practicality. The integrated parameterization-optimization technique efficiently reduces test scenarios without compromising critical performance aspects and deepens the understanding of system behavior under various conditions. Exploring diverse searching algorithms, particularly CT, enriches ADS development processes. The effective use of PSO in identifying critical scenarios and k-means clustering for directing search efforts highlights the potential of combining multiple methods. This research marks a pivotal step in ADS development, especially in scenario-based testing for ALKS, offering insights for more efficient ADS development and laying the groundwork for future refinements aligned with evolving ADS.

Keywords

automated driving systems, scenario parametrization, automated lane keeping systems, critical scenario generation, scenario-based testing

References

• [1] O.-R. A. D. ORAD Committee, Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. SAE International, 2021.
• [2] S. Wagner, A. Knoll, K. Groh, T. Kühbeck, D. Watzenig, and L. Eckstein, “Virtual assessment of automated driving: Methodology, challenges, and lessons learned,” SAE International Journal of Connected and Automated Vehicles, vol. 2, no. 12-02-04- 0020, pp. 263–277, 2019.
• [3] D. Kibalama, P. Tulpule, and B.-S. Chen, “Av/adas safety-critical testing scenario generation from vehicle crash data,” SAE Technical Paper, Tech. Rep., 2022.
• [4] W. Wachenfeld and H. Winner, “Die freigabe des autonomenfahrens,” Autonomes Fahren: technische, rechtliche und gesellschaftliche Aspekte, pp. 439–464, 2015.
• [5] A. Audi and A. Volkswagen, “The PEGASUS method,”
• [6] I. R. W. Group, “Guide for writing requirements,” INCOSE: San Diego, CA, USA, 2019.
• [7] ISO/IEC 15288, Iso/iec 15288, systems and software engineering-system life cycle processes, 2008.
• [8] C. Sippl, F. Bock, C. Lauer, A. Heinz, T. Neumayer, and R. German, “Scenario-based systems engineering: An approach towards automated driving function development,” in 2019 IEEE International Systems Conference (SysCon), IEEE, 2019, pp. 1–8.
• [9] C. Bergenhem, R. Johansson, A. Söderberg, et al., “How to reach complete safety requirement refinement for autonomous vehicles,” in CARS 2015-Critical Automotive applications: Robustness & Safety, 2015.
• [10] T. Menzel, G. Bagschik, and M. Maurer, “Scenarios for development, test and validation of automated vehicles,” in 2018 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2018, pp. 1821–1827.
• [11] M. Zipfl, N. Koch, and J. M. Zöllner, “A comprehensive review on ontologies for scenario-based testing in the context of autonomous driving,” arXiv preprint arXiv:2304.10837, 2023.
• [12] B. Schütt, S. Otten, and E. Sax, “Exploring the range of possible outcomes by means of logical scenario analysis and reduction for testing automated driving systems,” arXiv preprint arXiv:2306.12738, 2023.
• [13] M. Steimle, T. Menzel, and M. Maurer, “Toward a consistent taxonomy for scenario-based development and test approaches for automated vehicles: A proposal for a structuring framework, a basic vocabulary, and its application,” IEEE Access, vol. 9, pp. 147 828– 147 854, 2021.
• [14] ISO 21448, “Road vehicles-safety of the intended functionality,” Standard, International Organization for Standardization, Geneva, CH, 2019.
• [15] X. Zhang, J. Tao, K. Tan, et al., “Finding critical scenarios for automated driving systems: A systematic literature review,” arXiv preprint arXiv:2110.08664, 2021.
• [16] ASAM. “ASAM OpenSCENARIO.” Online; Accessed: 01.10.2022.
• [17] H. Felbinger, F. Klück, Y. Li, et al., “Comparing two systematic approaches for testing automated driving functions,” in 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), IEEE, 2019, pp. 1–6.
• [18] T. A. Dingus, S. G. Klauer, V. L. Neale, et al., “The 100-car naturalistic driving study, phase ii-results of the 100-car field experiment,” United States. Department of Transportation. National Highway Traffic Safety, Tech. Rep., 2006.
• [19] A. Zlocki, A. König, J. Bock, et al., “Logical scenarios parameterization for automated vehicle safety assessment: Comparison of deceleration and cutin scenarios from japanese and german highways,” IEEE Access, vol. 10, pp. 26 817–26 829, 2022.
• [20] J. Hiller, S. Koskinen, R. Berta, et al., “The l3pilot data management toolchain for a level 3 vehicle automation pilot,” Electronics, vol. 9, no. 5, p. 809, 2020.
• [21] Y. Barnard, S. Innamaa, S. Koskinen, H. Gellerman, E. Svanberg, and H. Chen, “Methodology for field operational tests of automated vehicles,” Transportation research procedia, vol. 14, pp. 2188–2196, 2016.
• [22] B. Schütt, J. Ransiek, T. Braun, and E. Sax, “1001 ways of scenario generation for testing of self-driving cars: A survey,” arXiv preprint arXiv:2304.10850, 2023.
• [23] J. Tao, Y. Li, F. Wotawa, H. Felbinger, and M. Nica, “On the industrial application of combinatorial testing for autonomous driving functions,” in 2019 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), IEEE, 2019, pp. 234–240.
• [24] L. Westhofen, C. Neurohr, T. Koopmann, et al., “Criticality metrics for automated driving: A review and suitability analysis of the state of the art,” Archives of Computational Methods in Engineering, vol. 30, no. 1, pp. 1–35, 2023.
• [25] L. González, E. Martí, I. Calvo, A. Ruiz, and J. Pérez, “Towards risk estimation in automated vehicles using fuzzy logic,” in Computer Safety, Reliability, and Security: SAFECOMP 2018 Workshops, ASSURE, DECSoS, SASSUR, STRIVE, and WAISE, Västerås, Sweden, September 18, 2018, Proceedings 37, Springer, 2018, pp. 278–289.
• [26] B. Huber, S. Herzog, C. Sippl, R. German, and A. Djanatliev, “Evaluation of virtual traffic situations for testing automated driving functions based on multidimensional criticality analysis,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020, pp. 1–7. DOI: 10.1109/ ITSC45102.2020.9294169.
• [27] F. Batsch, A. Daneshkhah, M. Cheah, S. Kanarachos, and A. Baxendale, “Performance boundary identification for the evaluation of automated vehicles using gaussian process classification,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, pp. 419–424. DOI: 10 . 1109 / ITSC . 2019. 8917119.
• [28] X. Hu, B. Zhu, D. Tan, N. Zhang, and Z. Wang, “Test scenario generation method for autonomous vehicles based on combinatorial testing and bayesian network,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, p. 09 544 070 221 125 523, 2022.
• [29] N. Zengin, O. Derebası, B. Öztürk, H. Kutucu, and S. Kınay, “Arama metotları kullanılarak aeb senaryolarına yönelik parametrizasyon tabanlı senaryo üretimi ve analizi parameterization based scenario generation and analysis for aeb scenarios by using search methods,” 2023.
• [30] UN Regulation 157, 157–uniform provisions concerning the approval of vehicles with regards to automated lane keeping systems [2021/389], 1 2021, 2022.
• [31] European Commission and Council, “Regulation (eu) 2012/1426 of the european parliament and of the council of 5 august 2022 on the the automated driving system (ads) of fully automated vehicles,” 2022.
• [32] ISO 34502, “Road vehicles — Scenario-based safety evaluation framework for automated driving systems,” International Organization for Standardization, Geneva, Switzerland, 2022.
• [33] ISO 34505, “Road vehicles — scenario evaluation and test case generation,” International Organization for Standardization, Geneva, Switzerland, 2022.
• [34] E. Knabe et al., “Environment simulator minimalistic (esmini),” Accessed on, vol. 20, 2021.
• [35] B. Huber, S. Herzog, C. Sippl, R. German, and A. Djanatliev, “Evaluation of virtual traffic situations for testing automated driving functions based on multidimensional criticality analysis,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2020, pp. 1–7.