Minhao Qiu, M. Sc.
Minhao Qiu is currently working as a Ph.D. student in the computer science department at the Friedrich-Alexander University Erlangen-Nürnberg (FAU) in cooperation with the AUDI AG. He received his Bachelor of Engineering degree in telecommunication engineering at the Zhejiang GongShang University in 2016. Afterwards, he studied Information and Communication Technology at the Friedrich-Alexander University Erlangen-Nürnberg in which received his Master of Science degree in 2019. His research interests include performance and dependability analysis of automated driving systems, machine learning, and deep learning.
Exploring the impact of scenario and distance information on the reliability assessment of multi-sensor systems
2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2022 (Maspalomas, Gran Canaria, 2022-08-31 - 2022-09-02)
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Associating sensor data and reference truth labels: A step towards SOTIF validation of perception sensors
Sixth IEEE International Workshop on Automotive Reliability, Test and Safety (ARTS)
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Multi-sensor system simulation based on RESTART algorithm
The 67th Annual Reliability and Maintainability Symposium
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Reliability assessment of multi-sensor perception system in automated driving functions
26th IEEE Pacific Rim International Symposium on Dependable Computing (PRDC 2021)
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Parameter tuning for a Markov-based multi-sensor system
2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2021
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Reliability design of multi-sensor systems
(Third Party Funds Single)Term: 2020-01-01 - 2022-12-31
Funding source: andere FörderorganisationModern driver assistance systems for self-driving cars often rely on data collected by different sensors to determine the necessary system decisions. To prevent system failures, different techniques can be used to enhance the reliability of such multi-sensor systems, e.g., aggregation, filtering, majority voting and other mechanisms for fault tolerance. As a consequence, erroneous sensing is rare but can be correlated in successive sensor readings (e.g., as error bursts) and also between sensors (e.g., in specific environmental conditions such as bad weather).
For a reliable design, error probabilities of such multi-sensor systems must be determined. In the project an existing analytical model based on Markov chain as well as a simulation model should be extended. This includes the following aspects: extensions for several correlated sensors, integration of practically relevant sensor fusion algorithms, consideration of environmental conditions, adaption of structure and parametrization of the error model, extensions of the simulation for rare events and inclusion of code, validation of the model results based on available data, and realization as a software tool for the reliability design of multi-sensor systems.
In this project, the preliminary work of the INI.FAU project is to be built and both the existing analytical model based on Markov chains and the simulation model for multi-sensor systems are to be expanded. The desired scientific knowledge consists in the further development of the analytical Markov model, which already takes into account bursts of errors of individual sensors and dependencies between two sensors, the expansion to more sensors, the consideration of further error prevention strategies and a tool implementation. Furthermore, knowledge of the use of rare event simulation is to be achieved in order to execute more detailed simulation models of multi-sensor systems in practical terms and thus to derive statistically reliable results. The simulation allows an even more realistic system simulation and a validation of the analytical modeling. A scientifically based methodology is developed to determine the reliability of multi-sensor systems.