Sensor data analysis is a crucial task for environmental cognition in smart traffic systems. Recently, vehicle-to-everything (V2X) collaborative analysis has leveraged intermediate feature communication between vehicles and infrastructure to achieve superior analysis performance compared to single-vehicle approaches. However, due to the limited bandwidth of V2X communication links, directly transmitting features can be inefficient, resulting in significant delays that are unacceptable for real-time decision-making. To address this challenge, we propose a compact feature representation method in the bird's eye view (BEV) space for communication-efficient collaborative analysis. As shown in Fig. 1, the proposed method can be viewed as a task-aware distributed coding approach with decoder side information. First, the ego vehicle and the networked infrastructure convert raw LiDAR data into BEV features using a shared PointPillars feature extractor. The infrastructure then applies the proposed BEV codec to transform these BEV features into a compact representation, encoding them into a binary bitstream through entropy coding based on the estimated distribution. The received features are subsequently warped and fused with the ego vehicle's features using a bidirectional attention fusion module, and processed by a single-shot detector to perform 3D object detection. Experimental results on the DAIR-V2X-C dataset demonstrate that the proposed framework achieves more than 1000 times compression compared to directly transmitting floating-point features, while maintaining high analysis performance in real-world V2X scenarios.

Point clouds offer the realistic three-dimensional (3-D) representation of objects or scenes at the expense of high data volume. To compactly represent such data in real-world applications, Video-based Point Cloud Compression (V-PCC) converts them into two-dimensional (2-D) attribute maps before lossy compression. However, the coding artifacts introduced in the decoded attribute maps eventually bring texture degradation in the reconstructed point cloud. In this paper, we propose a deep-learning based attribute map enhancement method by fully leveraging the guidance of the occupancy map in local feature modification and non-local attention for capturing long-range spatial correlations.