A Vehicle-Infrastructure Cooperative 3D Object Detection Scheme Based on Adaptive Feature Selection

LIANG Yan; YANG Huilin; SHAO Kai

doi:10.11999/JEIT250601

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LIANG Yan, YANG Huilin, SHAO Kai. A Vehicle-Infrastructure Cooperative 3D Object Detection Scheme Based on Adaptive Feature Selection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250601

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LIANG Yan, YANG Huilin, SHAO Kai. A Vehicle-Infrastructure Cooperative 3D Object Detection Scheme Based on Adaptive Feature Selection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250601

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A Vehicle-Infrastructure Cooperative 3D Object Detection Scheme Based on Adaptive Feature Selection

doi: 10.11999/JEIT250601 cstr: 32379.14.JEIT250601

LIANG Yan^{1, 2
,
,},
YANG Huilin¹,
SHAO Kai¹

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of Signal and Information Processing, Chongqing 400065, China

Funds: The Natural Science Foundation of Chongqing (CSTB2025NSCQ-GPX1253)

Received Date: 2025-06-25
Accepted Date: 2025-11-17
Rev Recd Date: 2025-11-17

Available Online: 2025-11-26

Abstract

Abstract

Objective Vehicle-infrastructure cooperative Three-Dimensional (3D) object detection is viewed as a core technology for intelligent transportation systems. As autonomous driving advances, the fusion of roadside and vehicle-mounted LiDAR data provides beyond-line-of-sight perception for vehicles, offering clear potential for improving traffic safety and efficiency. Conventional cooperative perception, however, is constrained by limited communication bandwidth and insufficient aggregation of heterogeneous data, which restricts the balance between detection performance and bandwidth usage. These constraints hinder the practical deployment of cooperative perception in complex traffic environments. This study proposes an Adaptive Feature Selection-based Vehicle-Infrastructure Cooperative 3D Object Detection Scheme (AFS-VIC3D) to address these challenges. Spatial filtering theory is used to identify and transmit the critical features required for detection, improving 3D perception performance while reducing bandwidth consumption. Methods AFS-VIC3D uses a coordinated design for roadside and vehicle-mounted terminals. Incoming point clouds are encoded into Bird’s-Eye View (BEV) features through PointPillar encoders, and metadata synchronization ensures spatiotemporal alignment. At the roadside terminal, key features are selected using two parallel branches: a Graph Structure Feature Enhancement Module (GSFEM) and an Adaptive Communication Mask Generation Module (ACMGM). Multi-scale features are then extracted hierarchically with a ResNet backbone. The outputs of both branches are fused through elementwise multiplication to generate optimized features for transmission. At the vehicle-mounted terminal, BEV features are processed using homogeneous backbones and fused through a Multi-Scale Feature Aggregation (MSFA) module across scale, spatial, and channel dimensions, reducing sensor heterogeneity and improving detection robustness. Results and Discussions The effectiveness and robustness of AFS-VIC3D are validated on both the DAIRV2X real-world dataset and the V2XSet simulation dataset. Comparative experiments (Table 1, Fig. 5) show that the model attains higher detection accuracy with lower communication overhead and exhibits slower degradation under low-bandwidth conditions. Ablation studies (Table 2) demonstrate that each module (GSFEM, ACMGM, and MSFA) contributes to performance. GSFEM improves the discriminability of target features, and ACMGM used with GSFEM further reduces communication cost. A comparison of feature transmission methods (Table 3) shows that adaptive sampling based on scene complexity and target density (C-DASFAN) yields higher accuracy and lower bandwidth usage, confirming the advantage of ACMGM. BEV visualizations (Fig. 6) indicate that predicted bounding boxes align closely with ground truth with minimal redundancy. Analysis of complex scenarios (Fig. 7) shows fewer missed detections and false positives, demonstrating robustness in high-density and complex road environments. Feature-level visualization (Fig. 8) further verifies that GSFEM and ACMGM enhance target features and suppress background noise, improving overall detection performance. Conclusions This study presents an AFS-VIC3D that addresses the key challenges of limited communication bandwidth and heterogeneous data aggregation through a coordinated design combining roadside dual-branch feature optimization and vehicle-mounted MSFA. The GSFEM module uses graph neural networks to enhance the discriminability of target features, the ACMGM module optimizes communication resources through communication mask generation, and the MSFA module improves heterogeneous data aggregation between vehicle and infrastructure terminals through joint spatial and channel aggregation. Experiments on the DAIR-V2X and V2XSet datasets show that AFS-VIC3D improves 3D detection accuracy while lowering communication overhead, with clear advantages in complex traffic scenarios. The framework offers a practical and effective solution for vehicle-infrastructure cooperative 3D perception and demonstrates strong potential for deployment in bandwidth-constrained intelligent transportation systems.
- Vehicle-infrastructure cooperation,
- 3D object detection,
- Communication bandwidth,
- Adaptive feature selection,
- Feature fusion

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References(24)

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