Semantic closed-loop based visual mapping algorithm for automated valet parking

Jiang, Haobin; Chen, Yixiao; Shen, Qingyuan; Yin, Chenhui; Cai, Junyu

doi:10.1177/09544070231167639

Cited by 2 publications

(1 citation statement)

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“…Intelligent networked vehicles represent an essential development direction in today’s cutting-edge science and technology, significantly impacting smart city road transportation, modern logistics, and other areas [ 1 , 2 , 3 ]. The environment perception system serves as the foundation and guarantee for the safe operation of intelligent vehicles on the road.…”

Section: Introductionmentioning

confidence: 99%

3D Object Detection under Urban Road Traffic Scenarios Based on Dual-Layer Voxel Features Fusion Augmentation

Jiang,

Ren,

2024

Sensors

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To enhance the accuracy of detecting objects in front of intelligent vehicles in urban road scenarios, this paper proposes a dual-layer voxel feature fusion augmentation network (DL-VFFA). It aims to address the issue of objects misrecognition caused by local occlusion or limited field of view for targets. The network employs a point cloud voxelization architecture, utilizing the Mahalanobis distance to associate similar point clouds within neighborhood voxel units. It integrates local and global information through weight sharing to extract boundary point information within each voxel unit. The relative position encoding of voxel features is computed using an improved attention Gaussian deviation matrix in point cloud space to focus on the relative positions of different voxel sequences within channels. During the fusion of point cloud and image features, learnable weight parameters are designed to decouple fine-grained regions, enabling two-layer feature fusion from voxel to voxel and from point cloud to image. Extensive experiments on the KITTI dataset demonstrate the significant performance of DL-VFFA. Compared to the baseline network Second, DL-VFFA performs better in medium- and high-difficulty scenarios. Furthermore, compared to the voxel fusion module in MVX-Net, the voxel feature fusion results in this paper are more accurate, effectively capturing fine-grained object features post-voxelization. Through ablative experiments, we conducted in-depth analyses of the three voxel fusion modules in DL-VFFA to enhance the performance of the baseline detector and achieved superior results.

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Section: Introductionmentioning

confidence: 99%