Evaluation of Camera Recognition Performance under Blockage Using Virtual Test Drive Toolchain.

This study is the first to develop technology to evaluate the object recognition performance of camera sensors, which are increasingly important in autonomous vehicles owing to their relatively low price, and to verify the efficiency of camera recognition algorithms in obstruction situations. To this end, the concentration and color of the blockage and the type and color of the object were set as major factors, with their effects on camera recognition performance analyzed using a camera simulator based on a virtual test drive toolkit. The results show that the blockage concentration has the largest impact on object recognition, followed in order by the object type, blockage color, and object color.

Experimental Analysis of Various Blockage Performance for LiDAR Sensor Cleaning Evaluation.

Autonomous driving includes recognition, judgment, and control technologies, and is implemented using sensors such as cameras, LiDAR, and radar. However, recognition sensors are exposed to the outside environment and their performance may deteriorate because of the presence of substances that interfere with vision, such as dust, bird droppings, and insects, during operation. Research on sensor cleaning technology to solve this performance degradation has been limited.