Abstract
Assuring an adequate level of safety is the key challenge for the approval of autonomous vehicles (AV). Dynamic Risk Assessment (DRA) enables AVs to assess the risk of the current situation instead of behaving according to worst-case expectations regarding all possible situations. While current DRA techniques typically predict the behavior of others based on observing kinematic states, Situation-Aware Dynamic Risk Assessment (SINADRA) uses probabilistic environmental knowledge about causal factors that indicate behavior changes before they occur. In this paper, we expand upon previous conceptual ideas and introduce an open-source Python software component that realizes the SINADRA pipeline including situation class detection, Bayesian network-based behavior intent prediction, trajectory distribution generation, and the final risk assessment. We exemplify the component’s usage by estimating front vehicle braking risks in the CARLA AV simulator.
The work presented in this paper was partially supported by the Intel Collaborative Research Institute ICRI SAVe (http://icri-save.de/).
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
Notes
- 1.
CARLA AV Simulator: https://carla.org/.
- 2.
SINADRA GitHub Repository: https://github.com/JaRei/sinadra.
- 3.
- 4.
References
Eggert, J.: Risk estimation for driving support and behavior planning in intelligent vehicles. at-Automatisierungstechnik 66(2), 119–131 (2018)
Feth, P.: Dynamic behavior risk assessment for autonomous systems. Dissertation, Technical University Kaiserslautern, Germany (2020)
Horst, R.: Time-to-collision as a cue for decision-making in braking. Vision in Vehicles-III (1991)
Pek, C., Manzinger, S., Koschi, M., Althoff, M.: Using online verification to prevent autonomous vehicles from causing accidents. Nat. Mach. Intell. 2(9), 518–528 (2020). https://doi.org/10.1038/s42256-020-0225-y
Reich, J., Trapp, M.: SINADRA: towards a framework for assurable situation-aware dynamic risk assessment of autonomous vehicles. In: 16th European Dependable Computing Conference (EDCC) (2020). https://doi.org/10.1109/EDCC51268.2020.00017
Schreier, M., Willert, V., Adamy, J.: An integrated approach to maneuver-based trajectory prediction and criticality assessment in arbitrary road environments. IEEE Trans. Intell. Transp. Syst. 17(10), 2751–2766 (2016)
Shalev-Shwartz, S., Shammah, S., Shashua, A.: On a formal model of safe and scalable self-driving cars. arXiv:1708.06374 (2017)
Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Phys. Rev. E 62(2), 1805 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Reich, J., Wellstein, M., Sorokos, I., Oboril, F., Scholl, KU. (2021). Towards a Software Component to Perform Situation-Aware Dynamic Risk Assessment for Autonomous Vehicles. In: Adler, R., et al. Dependable Computing - EDCC 2021 Workshops. EDCC 2021. Communications in Computer and Information Science, vol 1462. Springer, Cham. https://doi.org/10.1007/978-3-030-86507-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-86507-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86506-1
Online ISBN: 978-3-030-86507-8
eBook Packages: Computer ScienceComputer Science (R0)