Abstract
An Automated Driving System (ADS) must undergo comprehensive safety testing before receiving a road permit. Since it is not clear what exactly constitutes sufficient safety for an ADS, one could assume that an ADS is safe enough if it is at least as safe as a Human Driven Vehicle (HDV). Simulation-based testing is a cost-effective way to check the safety of an ADS. My goal is to develop an approach to compare the safety behavior of ADS and HDV using simulation. This comparison aims to quantify the advantages and disadvantages of ADS compared to HDV. Additionally, I aim to develop a process for selecting specific scenarios that contribute to building trust in the accuracy and reliability of simulation results. This involves defining performance criteria against which the behavior of an ADS in the simulator is compared to that of a HDV. Furthermore, I aim to translate the performance advantages or disadvantages observed in simulated ADS behavior into real-world safety-critical traffic scenarios.
Supported by Estonian Research Council grant PRG1226, Bolt Technology OÜ grant, and the Estonian state stipend for doctoral studies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The ADS under test.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
References
Althoff, M., Dolan, J.M.: Online verification of automated road vehicles using reachability analysis. IEEE Trans. Robot. 30(4), 903–918 (2014)
Ben Abdessalem, R., Nejati, S., Briand, L.C., Stifter, T.: Testing advanced driver assistance systems using multi-objective search and neural networks. In: Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, pp. 63–74 (2016)
Broggi, A., et al.: Extensive tests of autonomous driving technologies. IEEE Trans. Intell. Transp. Syst. 14(3), 1403–1415 (2013)
Broggi, A., Cerri, P., Felisa, M., Laghi, M.C., Mazzei, L., Porta, P.P.: The VisLab intercontinental autonomous challenge: an extensive test for a platoon of intelligent vehicles. Int. J. Veh. Auton. Syst. 10(3), 147–164 (2012)
Chen, Z., He, F., Yin, Y., Du, Y.: Optimal design of autonomous vehicle zones in transportation networks. Transp. Res. Part B: Methodological 99, 44–61 (2017)
Duarte, F., Ratti, C.: The impact of autonomous vehicles on cities: a review. J. Urban Technol. 25(4), 3–18 (2018)
Funke, J., et al.: Up to the limits: autonomous Audi TTS. In: 2012 IEEE Intelligent Vehicles Symposium, pp. 541–547. IEEE (2012)
Jha, S., et al.: Kayotee: a fault injection-based system to assess the safety and reliability of autonomous vehicles to faults and errors. arXiv preprint arXiv:1907.01024 (2019)
Nieuwenhuijze, M.R., van Keulen, T., Öncü, S., Bonsen, B., Nijmeijer, H.: Cooperative driving with a heavy-duty truck in mixed traffic: experimental results. IEEE Trans. Intell. Transp. Syst. 13(3), 1026–1032 (2012)
O’Kelly, M., Sinha, A., Namkoong, H., Tedrake, R., Duchi, J.C.: Scalable end-to-end autonomous vehicle testing via rare-event simulation. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Ploeg, J., Scheepers, B.T., Van Nunen, E., Van de Wouw, N., Nijmeijer, H.: Design and experimental evaluation of cooperative adaptive cruise control. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 260–265. IEEE (2011)
Wicker, M., Huang, X., Kwiatkowska, M.: Feature-guided black-box safety testing of deep neural networks. In: Beyer, D., Huisman, M. (eds.) TACAS 2018. LNCS, vol. 10805, pp. 408–426. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-89960-2_22
Acknowledgements
This research was partly funded by the Austrian BMK, BMAW, and State of Upper Austria under the SCCH competence center INTEGRATE [(FFG grant 892418)], the Estonian Research Council (grant PRG1226), Bolt Technology OÜ, and the Estonian state stipend for doctoral studies.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Khan, F., Anwar, H., Pfahl, D. (2024). Simulation-Based Safety Testing of Automated Driving Systems. In: Kadgien, R., Jedlitschka, A., Janes, A., Lenarduzzi, V., Li, X. (eds) Product-Focused Software Process Improvement. PROFES 2023. Lecture Notes in Computer Science, vol 14484. Springer, Cham. https://doi.org/10.1007/978-3-031-49269-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-49269-3_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49268-6
Online ISBN: 978-3-031-49269-3
eBook Packages: Computer ScienceComputer Science (R0)