Towards Efficient 3D Object Detection for Vehicle-Infrastructure Collaboration via Risk-Intent Selection

• URL: http://arxiv.org/abs/2601.03001v1
• Date: Tue, 06 Jan 2026 13:25:23 GMT
• Title: Towards Efficient 3D Object Detection for Vehicle-Infrastructure Collaboration via Risk-Intent Selection
• Authors: Li Wang, Boqi Li, Hang Chen, Xingjian Wu, Yichen Wang, Jiewen Tan, Xinyu Zhang, Huaping Liu,
• Abstract summary: Risk-intent Selective detection (RiSe) is an interaction-aware framework that shifts the paradigm from identifying visible regions to prioritizing risk-critical ones.<n>RiSe implements a semantic-selective fusion scheme that transmits high-fidelity features only from high-interaction regions.<n>Our method reduces communication volume to 0.71% of full feature sharing while maintaining state-of-the-art detection accuracy.
• Score: 23.186947629238233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicle-Infrastructure Collaborative Perception (VICP) is pivotal for resolving occlusion in autonomous driving, yet the trade-off between communication bandwidth and feature redundancy remains a critical bottleneck. While intermediate fusion mitigates data volume compared to raw sharing, existing frameworks typically rely on spatial compression or static confidence maps, which inefficiently transmit spatially redundant features from non-critical background regions. To address this, we propose Risk-intent Selective detection (RiSe), an interaction-aware framework that shifts the paradigm from identifying visible regions to prioritizing risk-critical ones. Specifically, we introduce a Potential Field-Trajectory Correlation Model (PTCM) grounded in potential field theory to quantitatively assess kinematic risks. Complementing this, an Intention-Driven Area Prediction Module (IDAPM) leverages ego-motion priors to proactively predict and filter key Bird's-Eye-View (BEV) areas essential for decision-making. By integrating these components, RiSe implements a semantic-selective fusion scheme that transmits high-fidelity features only from high-interaction regions, effectively acting as a feature denoiser. Extensive experiments on the DeepAccident dataset demonstrate that our method reduces communication volume to 0.71\% of full feature sharing while maintaining state-of-the-art detection accuracy, establishing a competitive Pareto frontier between bandwidth efficiency and perception performance.

Related papers

• Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA [61.12136997430116]
  Decentralized federated learning (DFL) based on low-rank adaptation (LoRA) enables mobile devices with multi-task datasets to collaboratively fine-tune a large language model (LLM) by exchanging locally updated parameters with a subset of neighboring devices via wireless connections for knowledge integration.<n> directly aggregating parameters fine-tuned on heterogeneous datasets induces three primary issues across the DFL life-cycle: (i) catastrophic knowledge forgetting during fine-tuning process, arising from conflicting update directions caused by data heterogeneity; (ii) textitinefficient communication and convergence during model aggregation process,
  arXiv  Detail & Related papers  (2026-02-24T02:45:32Z)
• A Secure and Private Distributed Bayesian Federated Learning Design [56.92336577799572]
  Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server.<n>DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy.<n>We propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration.
  arXiv  Detail & Related papers  (2026-02-23T16:12:02Z)
• Pedestrian Crossing Intention Prediction Using Multimodal Fusion Network [3.878105750489656]
  Pedestrian crossing intention prediction is essential for the deployment of autonomous vehicles (AVs) in urban environments.<n>This paper proposes a multimodal fusion network that leverages seven modality features from both visual and motion branches.<n>Experiments on the JAAD dataset validate the effectiveness of the proposed network, achieving superior performance compared to the baseline methods.
  arXiv  Detail & Related papers  (2025-11-25T07:18:12Z)
• MCOP: Multi-UAV Collaborative Occupancy Prediction [40.58729551462363]
  Current Bird's Eye View (BEV)-based approaches exhibit two main limitations.<n>We propose a novel multi-UAV collaborative occupancy prediction framework.<n>Our method achieves state-of-the-art accuracy, significantly outperforming existing collaborative methods.
  arXiv  Detail & Related papers  (2025-10-14T16:17:42Z)
• BEVUDA++: Geometric-aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection [56.477525075806966]
  Vision-centric Bird's Eye View (BEV) perception holds considerable promise for autonomous driving.<n>Recent studies have prioritized efficiency or accuracy enhancements, yet the issue of domain shift has been overlooked.<n>We introduce an innovative geometric-aware teacher-student framework, BEVUDA++, to diminish this issue.
  arXiv  Detail & Related papers  (2025-09-17T16:31:40Z)
• Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance [12.513296074529727]
  This paper proposes the Real-time Edge-based Autonomous Co-pilot Trajectory planner (REACT) for autonomous driving.<n>REACT is a V2X-integrated trajectory optimization framework for AD based on a fine-tuned lightweight Vision-Language Model (VLM)<n> evaluated on the DeepAccident benchmark, REACT achieves state-of-the-art performance, a 77% collision rate reduction, a 48.2% Video Panoptic Quality (VPQ), and a 0.57-second inference latency on the Jetson AGX Orin.
  arXiv  Detail & Related papers  (2025-08-01T20:16:04Z)
• Backscatter Device-aided Integrated Sensing and Communication: A Pareto Optimization Framework [59.30060797118097]
  Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban non-line-of-sight scenarios.<n>This paper proposes a backscatter approximation (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in environments of enhancement.
  arXiv  Detail & Related papers  (2025-07-12T17:11:06Z)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• Diffusion-Based Particle-DETR for BEV Perception [94.88305708174796]
  Bird-Eye-View (BEV) is one of the most widely-used scene representations for visual perception in Autonomous Vehicles (AVs) Recent diffusion-based methods offer a promising approach to uncertainty modeling for visual perception but fail to effectively detect small objects in the large coverage of the BEV. Here, we address this problem by combining the diffusion paradigm with current state-of-the-art 3D object detectors in BEV.
  arXiv  Detail & Related papers  (2023-12-18T09:52:14Z)
• Attention Based Feature Fusion For Multi-Agent Collaborative Perception [4.120288148198388]
  We propose an intermediate collaborative perception solution in the form of a graph attention network (GAT) The proposed approach develops an attention-based aggregation strategy to fuse intermediate representations exchanged among multiple connected agents. This approach adaptively highlights important regions in the intermediate feature maps at both the channel and spatial levels, resulting in improved object detection precision.
  arXiv  Detail & Related papers  (2023-05-03T12:06:11Z)
• Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes [82.4186966781934]
  We introduce a simple, efficient, and effective two-stage detector, termed as Ret3D. At the core of Ret3D is the utilization of novel intra-frame and inter-frame relation modules. With negligible extra overhead, Ret3D achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-08-18T03:48:58Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Bandwidth-Adaptive Feature Sharing for Cooperative LIDAR Object Detection [2.064612766965483]
  Situational awareness as a necessity in the connected and autonomous vehicles (CAV) domain. Cooperative mechanisms have provided a solution to improve situational awareness by utilizing high speed wireless vehicular networks. We propose a mechanism to add flexibility in adapting to communication channel capacity and a novel decentralized shared data alignment method.
  arXiv  Detail & Related papers  (2020-10-22T00:12:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.