Reliability assessment of multi-sensor perception system in automated driving functions

Qiu M, Bazan P, Antesberger T, Bock F, German R (2021)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2021

DOI: 10.1109/prdc53464.2021.00022

Abstract

Precise environment perception, which consists of multi-sensor systems, ensures the safety of the automated driving functions (ADFs). With the rapid evolution of sensor technologies, sensor data collection errors occur rarely. Nevertheless, accurate interpretation of the sensor data context, such as 3D multi-object tracking, is still full of challenges. Safety of the Intended Functionality (SOTIF) takes concern of the vulnerability of perception systems. The research of quantitative SOTIF analysis is still ongoing. In this paper, we propose a multi-sensor system model to observe both false negative and false positive errors in different field-of-views. Besides, we also extend a proven Markov-based approach, which takes dependencies between successive sensor errors and correlation between two dependent sensors into account, to model three correlated sensors sharing the same region of interest. In the end, we present a numerical example to illustrate the quantitative reliability analysis of a multi-sensor perception system according to SOTIF.

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Audi AG Germany (DE)

How to cite

APA:

Qiu, M., Bazan, P., Antesberger, T., Bock, F., & German, R. (2021). Reliability assessment of multi-sensor perception system in automated driving functions. In Proceedings of the 26th IEEE Pacific Rim International Symposium on Dependable Computing (PRDC 2021).

MLA:

Qiu, Minhao, et al. "Reliability assessment of multi-sensor perception system in automated driving functions." Proceedings of the 26th IEEE Pacific Rim International Symposium on Dependable Computing (PRDC 2021) 2021.

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