Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Determining the Tactical Challenge of Scenarios to Efficiently Test Automated Driving Systems (2404.02599v2)

Published 3 Apr 2024 in cs.SE and cs.RO

Abstract: The selection of relevant test scenarios for the scenario-based testing and safety validation of automated driving systems (ADSs) remains challenging. An important aspect of the relevance of a scenario is the challenge it poses for an ADS. Existing methods for calculating the challenge of a scenario aim to express the challenge in terms of a metric value. Metric values are useful to select the least or most challenging scenario. However, they fail to provide human-interpretable information on the cause of the challenge which is critical information for the efficient selection of relevant test scenarios. Therefore, this paper presents the Challenge Description Method that mitigates this issue by analyzing scenarios and providing a description of their challenge in terms of the minimum required lane changes and their difficulty. Applying the method to different highway scenarios showed that it is capable of analyzing complex scenarios and providing easy-to-understand descriptions that can be used to select relevant test scenarios.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (14)
  1. SAE International, “J3016_202104: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles,” https://doi.org/10.4271/J3016_202104, 2021.
  2. S. Riedmaier, T. Ponn, D. Ludwig, B. Schick, and F. Diermeyer, “Survey on scenario-based safety assessment of automated vehicles,” IEEE Access, vol. 8, pp. 87 456–87 477, 2020.
  3. International Organization for Standardization, “ISO 34501:2022: Road vehicles - Test scenarios for automated driving systems - Vocabulary,” https://www.iso.org/standard/78950.html, 2022.
  4. L. Westhofen, C. Neurohr, T. Koopmann, M. Butz, B. Schütt, F. Utesch, 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, pp. 1–35, 2023.
  5. S. Söntges and M. Althoff, “Computing the drivable area of autonomous road vehicles in dynamic road scenes,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, pp. 1855–1866, 2017.
  6. X. Wu, X. Xing, J. Chen, Y. Shen, and L. Xiong, “Risk assessment method for driving scenarios of autonomous vehicles based on drivable area,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC).   IEEE, 2022, pp. 2206–2213.
  7. S. Ulbrich, T. Menzel, A. Reschka, F. Schuldt, and M. Maurer, “Defining and substantiating the terms scene, situation, and scenario for automated driving,” in 2015 IEEE 18th international conference on intelligent transportation systems.   IEEE, 2015, pp. 982–988.
  8. T. Ponn, M. Breitfuß, X. Yu, and F. Diermeyer, “Identification of challenging highway-scenarios for the safety validation of automated vehicles based on real driving data,” in 2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER).   IEEE, 2020, pp. 1–10.
  9. D. Nalic, T. Mihalj, F. Orucevic, M. Schabauer, C. Lex, W. Sinz, et al., “Criticality assessment method for automated driving systems by introducing fictive vehicles and variable criticality thresholds,” Sensors (Basel, Switzerland), vol. 22, 2022.
  10. P. Junietz, F. Bonakdar, B. Klamann, and H. Winner, “Criticality metric for the safety validation of automated driving using model predictive trajectory optimization,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC).   IEEE, 2018, pp. 60–65.
  11. 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.
  12. M. Scholtes, L. Westhofen, L. R. Turner, K. Lotto, M. Schuldes, H. Weber, et al., “6-layer model for a structured description and categorization of urban traffic and environment,” IEEE Access, vol. 9, pp. 59 131–59 147, 2021.
  13. E. I. Liu, G. Würsching, M. Klischat, and M. Althoff, “CommonRoad-Reach: A toolbox for reachability analysis of automated vehicles,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC).   IEEE, 2022, pp. 2313–2320.
  14. M. Althoff, M. Koschi, and S. Manzinger, “CommonRoad: Composable benchmarks for motion planning on roads,” in 2017 IEEE Intelligent Vehicles Symposium (IV).   IEEE, 2017, pp. 719–726.

Summary

We haven't generated a summary for this paper yet.