3D object detection is an important task in autonomous driving to perceive the surroundings. Despite the excellent performance, the existing 3D detectors lack the robustness to real-world corruptions caused by adverse weathers, sensor noises, etc., provoking concerns about the safety and reliability of autonomous driving systems. To comprehensively and rigorously benchmark the corruption robustness of 3D detectors, in this paper we design 27 types of common corruptions for both LiDAR and camera inputs considering real-world driving scenarios. By synthesizing these corruptions on public datasets, we establish three corruption robustness benchmarks-KITTI-C, nuScenes-C, and Waymo-C. Then, we conduct large-scale experiments on 24 diverse 3D object detection models to evaluate their corruption robustness. Based on the evaluation results, we draw several important findings, including: 1) motion-level corruptions are the most threatening ones that lead to significant performance drop of all models; 2) LiDAR-camera fusion models demonstrate better robustness; 3) camera-only models are extremely vulnerable to image corruptions, showing the indispensability of LiDAR point clouds. We release the benchmarks and codes at https://github.com/kkkcx/ 3D_Corruptions_AD. We hope that our benchmarks and findings can provide insights for future research on developing robust 3D object detection models.

It is important to predict football players’ value, especially during transfer period. This paper uses the player information and value data of the game FIFA 18 as data source. It is able to realize the prediction of its players’ best positions and values. After reducing the dimensionality of the value prediction model, a cluster analysis on the player’s position is introduced, and then grid search method is adopted to adjust the Xgboost parameters, Finally, Xgboost method is used to predict the player’s worth. The experimental results show that certain accuracy is achieved, but t there is still room for improvement in the accuracy of prediction. Discussions based on experiment results are made.

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