Which2comm, a novel multi-agent 3D object detection framework leveraging object-level sparse features, consistently outperformed other state-of-the-art methods on both detection performance and communication cost, exhibiting superior robustness to real-world latency.
World4V2X is proposed, the first world model framework tailored for V2X cooperative perception, which introduces a spatial observability modeling module that defines spatial consistency boundaries to distinguish reliable regions from uncertain ones, thereby enabling spatial consistency modeling over multi-agent heterog...
Vehicle-to-everything (V2X) collaboration can alleviate the limited perception range and occlusion issues of single-agent autonomous driving. However, most existing cooperative studies still focus on single-frame perception, while the few works on joint cooperative perception and prediction largely rely on fixed-step,...
Comparisons with mainstream state-of-the-art (SOTA) algorithms confirm that the proposed SDM-RTDETR method strikes a competitive trade-off between detection accuracy and model inference efficiency, demonstrating substantial engineering applicability in complex autonomous driving environments.
Multi-Object Tracking (MOT) remains challenging due to object occlusion, complex motions, and detection unreliability in crowded scenarios. We propose an enhanced MOT framework integrating and optimizing state-of-the-art components, specifically Improved Detection Confidence Boost (IDCBoost) and Track-Perspective-Based...
MVXCC-NET is presented, a cross-modal 3D detection network for occluded objects based on dual-path information complementation and regional weight modeling, which improves the utilization efficiency of fused features, allowing visual semantic information and spatial geometric information to support each other.
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