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Volume: 33 | Article ID: art00010
Data driven degradation of automotive sensors and effect analysis
  DOI :  10.2352/ISSN.2470-1173.2021.17.AVM-180  Published OnlineJanuary 2021

Autonomous driving plays a crucial role to prevent accidents and modern vehicles are equipped with multimodal sensor systems and AI-driven perception and sensor fusion. These features are however not stable during a vehicle’s lifetime due to various means of degradation. This introduces an inherent, yet unaddressed risk: once vehicles are in the field, their individual exposure to environmental effects lead to unpredictable behavior. The goal of this paper is to raise awareness of automotive sensor degradation. Various effects exist, which in combination may have a severe impact on the AI-based processing and ultimately on the customer domain. Failure mode and effects analysis (FMEA) type approaches are used to structure a complete coverage of relevant automotive degradation effects. Sensors include cameras, RADARs, LiDARs and other modalities, both outside and in-cabin. Sensor robustness alone is a well-known topic which is addressed by DV/PV. However, this is not sufficient and various degradations will be looked at which go significantly beyond currently tested environmental stress scenarios. In addition, the combination of sensor degradation and its impact on AI processing is identified as a validation gap. An outlook to future analysis and ways to detect relevant sensor degradations is also presented.

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Sven Fleck, Benjamin May, Gwen Daniel, Chris Davies, "Data driven degradation of automotive sensors and effect analysisin Proc. IS&T Int’l. Symp. on Electronic Imaging: Autonomous Vehicles and Machines,  2021,  pp 180-1 - 180-8,

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