Empirical Analysis of Autonomous Vehicle’s LiDAR Detection Performance Degradation for Actual Road Driving in Rain and Fog

Abstract

Light detection and ranging (LiDAR) is widely used in autonomous vehicles to obtain precise 3D information about surrounding road environments. However, under bad weather conditions, such as rain, snow, and fog, LiDAR-detection performance is reduced. This effect has hardly been verified in actual road environments. In this study, tests were conducted with different precipitation levels (10, 20, 30, and 40 mm/h) and fog visibilities (50, 100, and 150 m) on actual roads. Square test objects (60 × 60 cm2) made of retroreflective film, aluminum, steel, black sheet, and plastic, commonly used in Korean road traffic signs, were investigated. Number of point clouds (NPC) and intensity (reflection value of points) were selected as LiDAR performance indicators. These indicators decreased with deteriorating weather in order of light rain (10–20 mm/h), weak fog (<150 m), intense rain (30–40 mm/h), and thick fog (≤50 m). Retroreflective film preserved at least 74% of the NPC under clear conditions with intense rain (30–40 mm/h) and thick fog (<50 m). Aluminum and steel showed non-observation for distances of 20–30 m under these conditions. ANOVA and post hoc tests suggested that these performance reductions were statistically significant. Such empirical tests should clarify the LiDAR performance degradation.

Keywords

• LiDAR
• weather condition
• detection performance
• detection indicator
• empirical test