In autonomous driving perception, the fusion of LiDAR and camera modalities has become the dominant paradigm for 3D object detection. However, current multi-modal frameworks heavily rely on massive visual backbones pretrained on 2D semantic tasks. This reliance introduces substantial parameter redundancy and a structural misalignment, as 2D priors are ill-equipped to handle the extreme sparsity of LiDAR projections required for Bird's-Eye-View geometry. To address this, we present DeGuNet, an ultra-compact and plug-and-play image backbone explicitly designed for depth-guided representation learning. By incorporating sparsity-aware feature extraction mechanisms, DeGuNet effectively aligns multi-view images with unstructured LiDAR depth while strictly preventing invalid-region contamination. Extensive experiments on the nuScenes dataset demonstrate DeGuNet's broad plug-and-play applicability and superior efficiency. When integrated into established baselines, it fundamentally eliminates architectural redundancy, reducing GPU memory consumption by up to 66.5% and achieving a 1.16x inference speedup. Concurrently, DeGuNet delivers up to a 6.20 absolute mAP gain, establishing a new paradigm for parameter-efficient multi-modal 3D perception.
Haifan Zhang, Yijing Wang, Peixi Peng et al.· 0 citations
Rapid advances in drone technology, combined with the growing congestion of terrestrial transport networks, are driving the emergence of the low-altitude economy. Uncrewed Aerial Vehicles (UAVs) are increasingly deployed for low-altitude economy applications such as urban logistics and transportation, yet their expansion is constrained by the scarcity of spectrum resources. Although Multi-Agent Reinforcement Learning (MARL) offers a promising decentralized approach to improve spectral efficiency of UAVs, existing MARL methods suffer from high training costs, often requiring extensive environmental interactions. To overcome these limitations, we propose a novel Multi-Agent Model-Based reinforcement learning algorithm for decentralized spectrum sharing among UAVs in the low-altitude economy, which we denote as MAMBA-UAV. Adopting a Centralized Training with Decentralized Execution (CTDE) paradigm, MAMBA-UAV equips each UAV with a learned world model that captures compact environmental representations and predicts system dynamics. These world models are then utilized during MARL training to simulate interactions, thereby reducing the reliance on repeated real-environment rollouts. Through comprehensive simulations, we demonstrate that MAMBA-UAV substantially reduces the number of environmental interactions required for UAVs to achieve competitive spectrum-sharing performance, lowering training costs while maintaining high performance.
Tianle Li, Peixi Peng, Qingyu Liu et al.· IEEE Transactions on Cogniti...· 0 citations