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Environmental sensing is an essential aspect of autonomous driving systems, with millimeter wave radar currently gaining attention in academic circles due to its unique physical properties that complement optical sensing techniques such as vision. Compared to cameras and LIDAR, millimeter wave radar is not limited by light and meteorological conditions, boasts high penetration capabilities, and can operate around the clock to identify objects. However, the larger wavelengths of millimeter wave signals present significant challenges such as sparse point clouds and multipath effects, resulting in lower accuracy in environmental sensing. To address this issue, this paper proposes a point cloud enhancement method based on a GAN-LSTM network that converts the sparse point cloud data into semantically informative RF images, thereby improving object recognition accuracy. The proposed method is evaluated on the CARRADA dataset, and the experimental results demonstrate an improvement in object classification accuracy by 7.86% compared to the current state-of-the-art methods. This approach can significantly enhance the accuracy of millimeter wave radar-based environmental sensing in autonomous driving systems, enabling safer and more reliable vehicle operation.
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