The field of autonomous driving perception is rapidly advancing, with a focus on improving the accuracy and efficiency of 3D object detection, semantic occupancy forecasting, and collaborative perception. Researchers are exploring new approaches, such as radiance fields, temporal transformers, and latent diffusion models, to enhance the performance of perception systems. Notably, the development of lightweight and adaptive frameworks is enabling real-time predictions and efficient weight updates, making them suitable for federated learning and real-world applications.

Some papers have made significant contributions to the field, including the proposal of innovative frameworks such as CP-Guard, which detects and eliminates malicious agents in collaborative perception systems, and OcRFDet, which achieves state-of-the-art performance in multi-view 3D object detection. The introduction of new metrics, such as latency-aware AP and planning-aware AP, is also providing a more comprehensive evaluation of real-time 3D object detection systems.