Contaminations on Lidar Sensor Covers: Performance Degradation Including Fault Detection and Modeling as Potential Applications

Lidar sensors play an essential role in the perception system of automated vehicles. Fault
Detection, Isolation, Identification, and Recovery (FDIIR) systems are essential for increasing the reliability
of lidar sensors. Knowing the influence of different faults on lidar data is the first crucial step towards
fault detection for lidar sensors in automated vehicles. We investigate the influences of sensor cover
contaminations on the output data, i.e., on the lidar point cloud and full waveform. Different contamination
types were applied (dew, dirt, artificial dirt, foam, water, and oil) and the influence on the output data of
the single beam lidar RIEGL LD05-A20 and the automotive mechanically spinning lidar Ouster OS1-64
was evaluated. The LD05-A20 measurements show that dew, artificial dirt, and foam lead to unwanted
reflections at the sensor cover. Dew, artificial dirt over the entire transmitter, and foam measurements lead
to severe faults, i.e., complete sensor blindness. The OS1-64 measurements also show that dew can lead
to almost complete sensor blindness. The results look promising for further studies on fault detection and
isolation, since the different contamination types lead to different symptom combinations.