RCQFusion: Radar and Camera Sensor Fusion With Joint Quantization for Robust Object Detection in Autonomous Driving

Object detection constitutes a crucial component for environment perception in autonomous driving systems, and the multimodal fusion of millimeter-wave radar and camera is an effective solution to improve the accuracy and robustness of object detection. However, inherent sparsity and multinoise of automotive radar data pose challenges to feature extraction, thereby resulting in low radar utilization. This limitation is further evidenced by existing fusion methods, where the decision weight assignment between the camera and the radar is unbalanced, and the decision contribution of radar often remains suboptimal in fusion processes. To address these issues, a novel radar–camera fusion framework with joint quantization for object detection is proposed. Specifically, considering the nonuniform density distribution and spatial sparsity of radar point clouds, an improved multidimensional density clustering algorithm is first developed to achieve effective noise filtering. Subsequently, a radar confidence factor is introduced based on the target point cloud’s scalability to expand radar features. Furthermore, a dual-channel joint quantization matching mechanism is devised to enhance the decision contribution of radar during the fusion process. Comprehensive experiment and ablation studies conducted on the nuScenes dataset demonstrate that the proposed method exhibits superior inference speed, and the mean average precision (mAP) outperforms comparison methods.