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In recent years, 3D object detection based on LiDAR point clouds is a key component of autonomous driving. In pursuit of enhancing the accuracy of 3D point cloud feature extraction and point cloud detection, this paper introduces a novel 3D object detection model, termed as Graph Self-Attention-RCNN (GA-RCNN). This model is designed to integrate voxel information and point location information, enhancing the quality of 3D object proposals while maintaining contextual accuracy. The first stage rectifies the previous approach that relied on local features for preselected boxes, overlooking crucial global contextual information. An improved method is suggested in this work, utilizing BEV to capture long-range dependencies via a cross-attention mechanism. The second stage addresses the overreliance on local neighborhood point feature extraction. The Graph Self-Attention Pooling method is proposed, characterized by its dynamic computation of contribution weights for inputs. This enhances the model's flexibility and generalization performance. Extensive evaluations on KITTI and Waymo datasets demonstrate GA-RCNN's superior accuracy compared to other methods, affirming its efficacy in 3D object detection. [ABSTRACT FROM AUTHOR]

Limitations are identified in the expressive capabilities of the deep feature extraction network employed in deep reinforcement learning (DRL), particularly in complex scenarios. Additionally, learning performance is negatively impacted by compound errors, with consideration given to the potential critical role of sample quality in learning outcomes. Therefore, how to improve the collision avoidance decision-making adaptive and effectiveness for autonomous ships navigating in various scenarios is crucial. To address these issues and overcome data acquisition difficulties, a method called rule-guided vision supervised learning (RGVSL) is proposed in this paper. Through a static collision avoidance decision-making task, a comparison is drawn between a deep feature extraction network and the Nature CNN in DQN, revealing shortcomings in the feature extraction of DRL. Additionally, a collision avoidance decision-making environment with the capability of generalization for maritime encounters is proposed, and the advantages of the method in terms of adaptability and learning cost are validated. Finally, it is demonstrated that the RGVSL method is equivalent to a DQN with γ set to 0, indicating a significant performance improvement without compound errors. Achieving an adaptive decision accuracy of over 90% in various encounter scenarios without retraining, this research substantially reduces learning costs. It can provide innovative and practical solutions for the technological development in the field of autonomous ship collision avoidance decision-making. • Revolutionizes DRL data by using explicit rules for diverse scenarios, boosting model performance. • Exposes Nature CNN's DQN inadequacy, emphasizing the need for effective feature expression in DRL. • RGVSL achieves 90% accuracy in diverse maritime scenarios, cutting retraining costs, enhancing performance. [ABSTRACT FROM AUTHOR]

6DoF camera localization is an important component of autonomous driving and navigation. Deep learning has achieved impressive results in localization, but its robustness in dynamic environments has not been adequately addressed. In this paper, we propose a framework based on hybrid attention mechanism which can be generally applied to existing CNN-based pose regressors to improve their robustness in dynamic environments. Specifically, we propose a novel Transformer Bottleneck (TBo) block including convolution, channel attention, and a position-aware self-attention mechanism, which extracts more geometrically robust features by capturing the corresponding long-term dependencies between pixels. Furthermore, we introduce shuffle attention (SA) before the pose regressor, which integrates feature information in both spatial and channel dimensions, forcing the network to learn geometrically robust features, reducing the effects of dynamic objects and illumination conditions to improve camera localization accuracy. We evaluate our method on commonly benchmarked indoor and outdoor datasets and the experimental results show that our proposed method can significantly improve localization performance compared compare favorably to contemporary pose regressors schemes. In addition, extensive ablation evaluations are conducted to prove the effectiveness of our proposed hybrid attention bottleneck block for pose regression networks. • We propose a novel Transformer Bottleneck block with self-attention and channel attention to overcome the limitations of convolution. This coupling allows them to be optimized in a mutually reinforcing manner, significantly improving fine-grained feature extraction for accurate localization. • We propose a novel end-to-end hybrid attention network for single image localization, which improves the accuracy and robustness of camera localization, especially in dynamic scenes. • We conduct extensive experiments in both indoor and outdoor dataset, which shows that our model performs better than the existing competitive methods. [ABSTRACT FROM AUTHOR]