With the rapid progress of intelligent vehicle technology, the accurate recognition of road surface types and conditions has emerged as a crucial technology for improving the safety and comfort levels in autonomous driving. This paper puts forward a multi-feature fusion approach for road surface identification. Relying on a 24 GHz millimeter-wave radar, statistical features are combined with wavelet transform techniques. This combination enables the efficient classification of diverse road surface types and conditions. Firstly, the discriminability of radar echo signals corresponding to different road surface types is verified via statistical analysis. During this process, six-dimensional statistical features that display remarkable differences are extracted. Subsequently, a novel radar data reconstruction approach is presented. This method involves fitting discrete echo signals into coordinate curves. Then, discrete wavelet transform is utilized to extract both low-frequency and high-frequency features, thereby strengthening the spatio-temporal correlation of the signals. The low-frequency information serves to capture general characteristics, whereas the high-frequency information reflects detailed features. The statistical features and wavelet transform features are fused at the feature level, culminating in the formation of a 56-dimensional feature vector. Four machine learning models, namely the Wide Neural Network (WNN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Kernel methods, are employed as classifiers for both training and testing purposes. Experiments were executed with 8865 samples obtained from a real-vehicle platform. These samples comprehensively represented 12 typical road surface types and conditions. The experimental outcomes clearly indicate that the proposed method is capable of attaining a road surface type identification accuracy as high as 94.2%. As a result, it furnishes an efficient and cost-efficient road perception solution for intelligent driving systems. This research validates the potential application of millimeter-wave radar in intricate road environments and offers both theoretical underpinning and practical support for the advancement of autonomous driving technology.

To address the challenges of insufficient multi-scale feature representation and limited local geometric modeling in sparse point cloud 3D object detection, this paper proposes an efficient sparse voxel detection network named SAVF-Net (Sparse Attention and Voxel Fusion Network). Built upon a fully sparse detection framework, SAVF-Net introduces two core components: a Neighborhood Sparse Attention (NSA) module and a Parallel Dual-Tower Multi-Scale Voxel Fusion (MSVF-SSM) module. The NSA module leverages a local self-attention mechanism based on sparse neighborhood indices to effectively capture geometric correlations, thereby enhancing the representation of small or boundary objects. Simultaneously, the MSVF-SSM module utilizes parallel sparse convolutions with varying dilation rates, integrated with a channel-spatial dual-attention mechanism, to adaptively fuse global semantic and local geometric features. Experimental results on the KITTI dataset demonstrate that SAVF-Net significantly outperforms mainstream methods, achieving improvements of approximately 2.20% in 3D mAP and 2.14% in BEV mAP. Notably, the model yields a 2%–4% accuracy gain in small-object categories such as pedestrians and cyclists. Furthermore, real-world field tests validate the model’s generalization and stability in complex dynamic scenes. SAVF-Net achieves superior accuracy and robustness while maintaining computational efficiency, providing strong support for autonomous driving perception systems.