Autonomous driving paper index
P‐19.8: 3D Object Detection Data Improvement Based on LiDAR for Autonomous Driving in Adverse Weather Conditions
One-line summary
The current three‐dimensional target detection algorithm for smart cars mainly uses three‐dimensional point cloud data collected by LiDAR.
Engineering notes
Key topics: autonomous driving, 3d object detection, object detection, lidar, point cloud. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
The current three‐dimensional target detection algorithm for smart cars mainly uses three‐dimensional point cloud data collected by LiDAR. Effectively detect target objects in three‐dimensional space by applying various deep learning algorithms. However, under adverse weather conditions (such as rain, snow, fog, etc.), point cloud data collected by LiDAR may be distorted due to scattering and refraction. This affects the performance of the three‐dimensional target detection algorithm. This may lead to missed or incorrect detection of target objects, thereby affecting the driving safety and riding comfort of smart cars. This paper introduces two mainstream data processing strategies, namely data enhancement method in the model training stage and data preprocessing method in the model testing stage. Combine the two to improve model performance under the influence of adverse weather.
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