BEV feature exchange pyramid networks-based 3D object detection in small and distant situations: A decentralized federated learning framework

3D object detection, whose task is to perceive the surrounding environment, plays a significant role in autonomous driving. In this study, we propose a new BEV-FePNet 3D detection model, which can effectively fuse multi-modal information in deeply abstract features. The BEV-FePNet has been validated experimentally on the nuScenes dataset, and the findings demonstrate that the proposed approach enhances the performance of the detector and obtains 71.6 % mAP detection performance. In addition, with the rapid development of the autonomous driving market, collecting a large amount of data for autonomous driving has become one of the important means to enhance 3D detecting models' efficiency. However, local national data security policies have to be considered when autonomous driving manufacturers collect data in different countries, so it is difficult to transmit data abroad. To solve this problem, the DP-DeceFL framework has been proposed in this paper that utilizes differential privacy processing to enable information exchange between different countries without revealing sensitive information. Through the verification of nuScenes data, our proposed framework is superior to some selected federated learning frameworks.