MKD-Cooper: Cooperative 3D Object Detection for Autonomous Driving via Multi-Teacher Knowledge Distillation

Accurately detecting objects in 3D point clouds is critical for achieving precise scene understanding in autonomous driving systems. Cooperative perception, through information exchange among neighboring vehicles, can significantly improve object detection performance even under occlusion. This article proposes a novel cooperative perception framework based on multi-teacher knowledge distillation for 3D object detection, namely MKD-Cooper. First, we design a Collaborative Attention Fusion (CAF) module that dynamically captures inter-vehicle interactions through channel and spatial attention. By incorporating the CAF module into the CAF network, we effectively aggregate shared deep learning-based features from neighboring vehicles, resulting in a fused feature map that contains rich contextual information. Second, we propose an adaptive multi-teacher knowledge distillation method that adaptively assigns weights to different teacher models based on their current performance, effectively transferring valuable knowledge from multiple excellent teacher models to the student model. Experimental results on the OPV2V and V2XSim 2.0 datasets demonstrate that our method achieves state-of-the-art performance in detection accuracy while exhibiting excellent comprehensive performance between detection accuracy and efficiency. Moreover, field experiments in real urban environments further validate the effectiveness of our approach.