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3D-LiDAR-based cooperative perception has been generating significant interest for its ability to tackle challenges such as occlusion, sparse point clouds, and out-of-range issues that can be problematic for single-vehicle perception. Despite its effectiveness in overcoming various challenges, cooperative per-ception's performance can still be affected by the aforementioned issues when Connected Automated Vehicles (CAVs) operate at the edges of their sensing range. Our proposed approach called HYDRO-3D aims to improve object detection performance by explicitly incorporating historical object tracking information. Specifically, HYDRO-3D combines object detection features from a state-of-the-art object detection algorithm (V2X-ViT) with historical information from the object tracking algorithm to infer objects. Afterward, a novel spatial-temporal 3D neural network performing global and local manipulations of object-tracking historical data is applied to generate the feature map to enhance object detection. The proposed HYDRO-3D method is comprehensively evaluated on the state-of-the-art V2XSet. The qualitative and quantitative experiment results demonstrate that the HYDRO-3D can effectively utilize the object tracking information and achieve robust object detection performance. It outperforms the SOTA V2X-ViT by 3.7% in AP@0.7 of object detection for CAVs and can also be generalized to single-vehicle object detection with 4.5% improvement in AP@0.7.
Meng et al. (Mon,) studied this question.