Failure Mode Investigation to Enable LiDAR Health Monitoring for Automotive Application



Published Oct 26, 2023
Fred Chang Ehsan Jafarzadeh Jacqueline Del Gatto Graham Cran Hossein Sadjadi


Light Detection and Ranging (LiDAR) sensors are critical components of the perception system and play a significant role in enabling fully autonomous driving. Given that LiDARs have a higher failure rate than other sensors, such as camera and radar, it is crucial to monitor the health of this component to increasing the availability of autonomous driving features. Such a health monitoring system can additionally provide cost-effective maintenance for retail and fleet, improve the service experience of retail customers, and ensure the fidelity of the data produced by the LiDAR for engineering development. Since LiDAR is a relatively new technology, there is currently limited work in the area of LiDAR health monitoring. The failure modes and degradation behavior of these components have not been thoroughly studied in the literature for automotive applications. Therefore, this paper reviews LiDAR external and internal failure modes and their impacts on the perception performance. The external failure modes are categorized into multiple fault classes such as sensor blockage due to a layer of debris on the sensor, mechanical damage to the sensor cover, and mounting issues. The internal faults corresponding to LiDAR subcomponents such as transmitter, receiver or scanning mechanism, are explored for these LiDAR types: mechanical spinning, flash LiDAR, Micro-opto-electromechanical mirror LiDAR, and micromotion technology LiDAR. The failure modes of each subcomponent are also investigated to determine if they can be categorized as slow degradation or sudden failure. It is concluded that mechanical spinning LiDARs are expected to have higher failure rates than solid-state LiDARs. Both internal and external LiDAR failure modes can lead to reduced accuracy and reliability in detecting objects and obstacles, compromising the safety of autonomous driving systems, and increasing the possibility of collisions.

How to Cite

Chang, F. ., Jafarzadeh, E., Del Gatto, J., Cran, G. ., & Sadjadi, H. (2023). Failure Mode Investigation to Enable LiDAR Health Monitoring for Automotive Application. Annual Conference of the PHM Society, 15(1).
Abstract 279 | PDF Downloads 209



Lidar, Prognostics, Failure mode, Health monitoring

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