Joint Camera and LiDAR Risk Analysis

Oliver Zendel, Johannes Huemer, Markus Murschitz, Gustavo Fernandez Dominguez, Amadeus Lobe; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 88-97


Sensor fusion of camera-based sensors and LiDAR is widely used in autonomous systems with the premise of increased robustness. Contrarily, due to their overlapping functional principles, they also share many risk factors that may result in degraded operation. This work applies the risk analysis method Hazard and Operability study (HAZOP) to LiDAR sensors and connects it with an existing camera-based HAZOP. This systematic approach leads to a structured listing of potential sources of data quality reduction in LiDAR data. Many risk factors identified for camera-based systems (e.g. transparency or reflections) can be correlated to degradation in the corresponding LiDAR data. To validate our findings, the public dataset A2D2 is analyzed for such co-occurring camera-LiDAR risk factors. Additionally, experiments under controlled laboratory conditions are performed to quantify the impact of various identified risks. Our HAZOP results are released publicly and are intended to improve the design and usage of sensor systems as well as training and test datasets for safer autonomous systems.

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@InProceedings{Zendel_2023_CVPR, author = {Zendel, Oliver and Huemer, Johannes and Murschitz, Markus and Dominguez, Gustavo Fernandez and Lobe, Amadeus}, title = {Joint Camera and LiDAR Risk Analysis}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {88-97} }