Abstract
The performance of LiDAR sensors deteriorates under adverse weather conditions such as rainfall. However, few studies have empirically analyzed this phenomenon. Hence, we investigated differences in sensor data due to environmental changes (distance from objects (road signs), object material, vehicle (sensor) speed, and amount of rainfall) during LiDAR sensing of road facilities. The indicators used to verify the performance of LiDAR were numbers of point cloud (NPC) and intensity. Differences in the indicators were tested through a two-way ANOVA. First, both NPC and intensity increased with decreasing distance. Second, despite some exceptions, changes in speed did not affect the indicators. Third, the values of NPC do not differ depending on the materials and the intensity of each material followed the order aluminum > steel > plastic > wood, although exceptions were found. Fourth, with an increase in rainfall, both indicators decreased for all materials; specifically, under rainfall of 40 mm/h or more, a substantial reduction was observed. These results demonstrate that LiDAR must overcome the challenges posed by inclement weather to be applicable in the production of road facilities that improve the effectiveness of autonomous driving sensors.
Highlights
light detection and ranging (LiDAR) in Real Road DrivingAround the world, technological advancements are being made to expedite the commercialization of automated vehicles
Order to identify this problem, this study explores the performance of LiDAR systems systems when driving in a real-road environment by selecting performance indicators that when driving in a real-road environment by selecting performance indicators that can can be quantified for objectifying the results of performance verification
It aims to examine how the qualitative characteristics of LiDAR identified through literature are reflected in the real road driving environment by analyzing the performance of LiDAR based on quantitative performance indicators
Summary
LiDAR in Real Road DrivingAround the world, technological advancements are being made to expedite the commercialization of automated vehicles. The sensor aspect involves technologies for collecting information about surrounding conditions and objects; the perception and detection aspect involves technologies for classifying and tracking objects based on the collected data; and the control part involves technologies for controlling a vehicle based on the information about the classified object In addition to these technologies, there is a pressing need for the commercialization of technologies for communication, precision maps, and roads and road infrastructure for connected and automated vehicles [2]. LiDAR, which has advanced rapidly in recent years, has positive characteristics such as excellent object detection, high detection accuracy, and high performance even under low-light conditions [4]. Because of these advantages, LiDAR is the most suitable sensor for automated vehicles and is being actively used by automakers such as Google Waymo and Volvo. The scattering of the laser caused by raindrops interferes with the object detection, with errors having been noted as the measurement distance to an object increases [7]
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