Modular Fault Diagnosis Framework for Complex Autonomous Driving Systems
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909389938491392 |
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| author | Orf, Stefan Ochs, Sven Doll, Jens Schotschneider, Albert Heinrich, Marc Zofka, Marc René Zöllner, J. Marius |
| author_facet | Orf, Stefan Ochs, Sven Doll, Jens Schotschneider, Albert Heinrich, Marc Zofka, Marc René Zöllner, J. Marius |
| contents | Fault diagnosis is crucial for complex autonomous mobile systems, especially for modern-day autonomous driving (AD). Different actors, numerous use cases, and complex heterogeneous components motivate a fault diagnosis of the system and overall system integrity. AD systems are composed of many heterogeneous components, each with different functionality and possibly using a different algorithm (e.g., rule-based vs. AI components). In addition, these components are subject to the vehicle's driving state and are highly dependent. This paper, therefore, faces this problem by presenting the concept of a modular fault diagnosis framework for AD systems. The concept suggests modular state monitoring and diagnosis elements, together with a state- and dependency-aware aggregation method. Our proposed classification scheme allows for the categorization of the fault diagnosis modules. The concept is implemented on AD shuttle buses and evaluated to demonstrate its capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09643 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modular Fault Diagnosis Framework for Complex Autonomous Driving Systems Orf, Stefan Ochs, Sven Doll, Jens Schotschneider, Albert Heinrich, Marc Zofka, Marc René Zöllner, J. Marius Robotics Fault diagnosis is crucial for complex autonomous mobile systems, especially for modern-day autonomous driving (AD). Different actors, numerous use cases, and complex heterogeneous components motivate a fault diagnosis of the system and overall system integrity. AD systems are composed of many heterogeneous components, each with different functionality and possibly using a different algorithm (e.g., rule-based vs. AI components). In addition, these components are subject to the vehicle's driving state and are highly dependent. This paper, therefore, faces this problem by presenting the concept of a modular fault diagnosis framework for AD systems. The concept suggests modular state monitoring and diagnosis elements, together with a state- and dependency-aware aggregation method. Our proposed classification scheme allows for the categorization of the fault diagnosis modules. The concept is implemented on AD shuttle buses and evaluated to demonstrate its capabilities. |
| title | Modular Fault Diagnosis Framework for Complex Autonomous Driving Systems |
| topic | Robotics |
| url | https://arxiv.org/abs/2411.09643 |