Juan Liu

Roadside perception systems, also known as roadside units (RSUs), are critical in Vehicle-to-Everything (V2X) applications, yet spatio-temporal asynchrony between multiple sensors severely compromises the accuracy of fusion. In this paper, a spatio-temporal synchronization method for millimeter-wave (MMW) radar and camera fusion is proposed, integrating target matching based on dynamic time warping (DTW) with spatio-temporal parameter estimation. Leveraging the advantages of DTW in time-series alignment to calculate the similarity between radar and visual trajectories enables target matching and parameter estimation in sparse scenes. This method was validated on a real-world dataset containing over 30 pedestrian trajectories, covering scenarios with varying densities ranging from one to six pedestrians. The results indicate a temporal offset of 0.116 s between the camera and radar. Following synchronization, the average spatial deviation decreased from 1.4358 to 0.1074 m in the x-direction (i.e., across the road) and from 3.0732 to 0.1775 m in the y-direction (i.e., along the road). Consequently, this method provides an efficient solution for deploying roadside perception systems in sparse traffic environments.

Original high-definition radar data contains rich environmental information, including distance, Doppler velocity, and azimuth. However, extracting robust features from such sparse and noisy frequency-domain data remains a challenge. To address this issue, this paper proposes an improved multi-task network, the Efficient Feature Aggregation with Balanced Attention Radar Network (EFA-RadNet). This network introduces the VoVNetV2 architecture into the field of raw radar perception and effectively preserves feature diversity across different receptive fields through a One-Shot Aggregation (OSA) module, avoiding signal aliasing. In addition, we propose an attention mechanism module, Balanced effective Squeeze–Excitation (B-eSE), which is better suited for sparse radar processing and effectively addresses the problem of weak target loss in the radar spectrum. Experiments on the RADIal dataset show that our EFA-RadNet achieves excellent target detection performance while also attaining optimal accuracy in free space segmentation.