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2019 | OriginalPaper | Chapter

An Approach for Validating Safety of Perception Software in Autonomous Driving Systems

Authors : Deepak Rao, Plato Pathrose, Felix Huening, Jithin Sid

Published in: Model-Based Safety and Assessment

Publisher: Springer International Publishing

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Abstract

The increasing complexity of Advanced Driver Assistance Systems (ADAS) presents a challenging task to validate safe and reliable performance of these systems under varied conditions. The test and validation of ADAS/AD with real test drives, although important, involves huge costs and time. Simulation tools provide an alternative with the added advantage of reproducibility but often use ideal sensors, which do not reflect real sensor output accurately. This paper presents a new validation methodology using fault injection, as recommended by the ISO 26262 standard, to test software and system robustness. In our work, we investigated and developed a tool capable of inserting faults at different software and system levels to verify its robustness. The scope of this paper is to cover the fault injection test for the Visteon’s DriveCore™ system, a centralized domain controller for Autonomous driving which is sensor agnostic and SoC agnostic. With this new approach, the validation of safety monitoring functionality and its behavior can be tested using real-world data instead of synthetic data from simulation tools resulting in having better confidence in system performance before proceeding with in-vehicle testing.

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Literature
1.
go back to reference Ziade, H., Ayoubi, R., Velazco, R.: A survey on fault injection techniques. Int. Arab J. Inf. Technol. 1, 171–186 (2004) Ziade, H., Ayoubi, R., Velazco, R.: A survey on fault injection techniques. Int. Arab J. Inf. Technol. 1, 171–186 (2004)
2.
go back to reference Reway, F., Huber, W., Ribeiro, E.P.: Test methodology for vision-based ADAS/AD algorithms with an automotive camera-in-the-loop. In: International Conference on Vehicular Electronics and Safety (ICVES), pp. 1–7. IEEE, Madrid (2018) Reway, F., Huber, W., Ribeiro, E.P.: Test methodology for vision-based ADAS/AD algorithms with an automotive camera-in-the-loop. In: International Conference on Vehicular Electronics and Safety (ICVES), pp. 1–7. IEEE, Madrid (2018)
3.
go back to reference Uriagereka, G.J., Lattarulo, R., Rastelli, J.P., Calonge, E.A., Ruiz Lopez, A., Espinoza Ortiz, H.: Fault injection method for safety and controllability evaluation of automated driving. In: Intelligent Vehicles Symposium (IV), pp. 1867–1872. IEEE, Los Angeles (2017) Uriagereka, G.J., Lattarulo, R., Rastelli, J.P., Calonge, E.A., Ruiz Lopez, A., Espinoza Ortiz, H.: Fault injection method for safety and controllability evaluation of automated driving. In: Intelligent Vehicles Symposium (IV), pp. 1867–1872. IEEE, Los Angeles (2017)
6.
go back to reference Koopman, P., Wagner, M.: Challenges in autonomous vehicle testing and validation. SAE Int. J. Transp. Saf. 4, 15–24 (2016)CrossRef Koopman, P., Wagner, M.: Challenges in autonomous vehicle testing and validation. SAE Int. J. Transp. Saf. 4, 15–24 (2016)CrossRef
8.
go back to reference Mikołajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: International Interdisciplinary Ph.D. Workshop (IIPhDW), Swinoujście, pp. 117–122 (2018) Mikołajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: International Interdisciplinary Ph.D. Workshop (IIPhDW), Swinoujście, pp. 117–122 (2018)
9.
go back to reference Boncelet, C.: The Essential Guide to Image Processing. Academic Press, Inc., Orlando (2009) Boncelet, C.: The Essential Guide to Image Processing. Academic Press, Inc., Orlando (2009)
10.
go back to reference Geiger, A., Lenz, P., Urtasun, R.: Are we ready for Autonomous Driving? In: The KITTI Vision Benchmark Suite. Conference on Computer Vision and Pattern Recognition (CVPR) (2012) Geiger, A., Lenz, P., Urtasun, R.: Are we ready for Autonomous Driving? In: The KITTI Vision Benchmark Suite. Conference on Computer Vision and Pattern Recognition (CVPR) (2012)
13.
go back to reference Wen, L.: UA-DETRAC: new benchmark and protocol for multi-object detection and tracking. arXiv CoRR, abs/1511.04136 (2015) Wen, L.: UA-DETRAC: new benchmark and protocol for multi-object detection and tracking. arXiv CoRR, abs/1511.04136 (2015)
Metadata
Title
An Approach for Validating Safety of Perception Software in Autonomous Driving Systems
Authors
Deepak Rao
Plato Pathrose
Felix Huening
Jithin Sid
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-32872-6_20

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