Parameter tuning for a Markov-based multi-sensor system

Qiu M, Kryda M, Bock F, Antesberger T, Straub D, German R (2021)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2021

DOI: 10.1109/SEAA53835.2021.00052

Abstract

Multi-sensor systems are the key components of automated driving functions. They enhance the quality of the driving experience and assisting in preventing traffic accidents. Due to the rapid evolution of sensor technologies, sensor data collection errors occur rarely. Nonetheless, according to Safety Of The Intended Functionality (SOTIF), an erroneous interpretation of the sensor data can also cause safety hazards. For example the front-camera may not understand the meaning of a traffic sign. Due to safety concerns it is essential to analyze the system reliability throughout the whole development process. In this work, we present an approach to explore the sensor's features, such as the dependencies between successive sensor detection errors and the correlation between different sensors on the KITTI dataset quantitatively. Besides, we apply the learned parameters to a proven multi-sensor system model, which is based on Discrete-time Markov chains, to estimate the reliability of a hypothetical Stereo camera-LiDAR based sensor system.

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How to cite

APA:

Qiu, M., Kryda, M., Bock, F., Antesberger, T., Straub, D., & German, R. (2021). Parameter tuning for a Markov-based multi-sensor system. In Proceedings of the 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2021.

MLA:

Qiu, Minhao, et al. "Parameter tuning for a Markov-based multi-sensor system." Proceedings of the 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2021 2021.

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