A study on self-diagnosis/prediction technology for LIDAR sensor of autonomous vehicles



Published Sep 4, 2023
Jaewook Lee Jongsoo Lee


Along with the development of autonomous driving technology, the need for self-failure diagnosis and Remaining Useful Life (RUL) prediction technology for core parts for autonomous driving is increasing. In particular, the characteristics of the light detection and ranging (LIDAR) sensor exposed to the outside further increase the need to apply fault diagnosis and RUL prediction technology considering various environmental variables. In this study, based on the accelerated degradation test of LIDAR, the failure mode was analyzed. Through this, LIDAR failure due to thermal runaway, which is the first failure type in high temperature conditions, was identified, and whether there were major environmental data that could identify thermal runaway was identified. In the case of LIDAR's thermal runaway phenomenon, a study on an algorithm to identify the precursor symptoms of failure in an accidental failure situation is conducted. Afterwards, through the actual vehicle test process, various environmental variable information is analyzed for correlation with LIDAR internal sensor data, and the abnormal data for the temperature of the internal parts of the LIDAR is predicted through the external environmental sensor.  

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Self-diagnosis, Autonomous driving vehicles, Lidar Sensor, Diagnosis-database

Cao, M., Wang, R., Chen, N., & Wang, J. (2022). A Learning-based Vehicle Trajectory-Tracking Approach for Autonomous Vehicles with LiDAR Failure under Various Lighting Conditions. IEEE/ASME transactions on mechatronics, vol. 27, No. 2. doi:10.1109/TMECH.2021.3077388

Jiang, L., Deng, Z., Tang, X., Hu, L., Lin, X., & Hu, X. (2021). Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data. Energy, vol. 234, doi:10.1016/j.energy.2021.121266

Lim, H., Chae, H., Lee, M., & Lee, K. (2017). Development and validation of safety performance evaluation scenarios of autonomous vehicle based on driving data. Journal of Auto-Vehicle Safety Association, vol. 9, no. 4, pp. 7-13. doi:10.22680.kasa.2017.9.4.007

Li, Y., & Olson, E. (2010). Extracting general-purpose features from LIDAR data. IEEE International Conference on Robotics and Automation, doi:10.1109/ROBOT.2010.5509690

Shan, J. & Sampath, A. (2005). Urban DEM Generation from Raw Lidar Data: A Labeling Algorithm and its Performance. Photogrammetric Engineering and Remote Sensing 71(2):217-226. doi:10.14358/PERS.71.2.217
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