AutoSAR Dataset

In current development of autonomous driving technology, understanding image-like representations, such as optical images, is essential for enhancing the reliable environmental perception. However, under harsh weather conditions, typical sensors in artificial vision, often fail to meet the demands of accurate target recognition. Synthetic aperture radar (SAR) technology, which can deliver high-resolution images under all weather conditions, is being increasingly integrated into automotive systems. However, the challenges like dynamic trajectory estimation and real-time SAR imaging remain significant obstacles. In this paper, aimed at providing timely and high-quality SAR images for driving environment, a novel vehicular SAR imaging approach is proposed. By employing sub-aperture scheme for SAR system, the approach eliminates the need for complicated and timeconsuming range cell migration and motion correction. Due to the short coherent accumulation, the instant range doppler algorithm enables high efficient SAR image generation while maintaining two-dimensional (2D) high-resolution performance, which allows precise target detection. The theoretical analysis and experimental results confirm the effectiveness of the proposed scheme. Then, a novel benchmark is established based on the proposed millimeter-wave (mmW) SAR approach, to thoroughly cover the SAR images in real-time environment. To validate the radar data, diverse targets in SAR images are annotated, providing a robust database support for the automotive SAR target recognition. Furthermore, a variety of mainstream deep learning based-methods are performed to complete the SAR vision understanding task, facilitating the practical application in autonomous driving.