Multi-modality sensor fusion for 3D perception is a significant part for autonomous driving perception, which enables a comprehensive integration of different sensors and obtains a holistic understanding of the surrounding environment to improve accuracy, stability and reliability. Nevertheless, vibrations, collisions, and acceleration/deceleration in motion may result in a minor disturbance to the position of sensors, leading to offsets in sensor calibration. To address the limitations of the original method demanding considerable manual effort and time for hand-annotating checkerboards, we introduce an online Camera-LiDAR calibration method, which can perform calculations during the operation of autonomous vehicles to eliminate sensor biases. Different from the previous CNN-based online calibration algorithm, our approach differs in utilizing multi-scale features to accomplish alignment with a foundational ResNet and FPN Backbone alongside an attention-based multi-scale feature fusion module. Moreover, in the design of the loss function, we incorporate depth loss and point cloud loss in addition to the original smooth L1 norm loss to facilitate network backpropagation. Ultimately, our proposed method achieves an average translation error of 0.88 cm and a rotation error of 0.073° on the KITTI odometry dataset, which can significantly limit the discrepancies between sensors and thus enhance detection accuracy.

In the contemporary social environment, social crisis events occur frequently with significant impacts. Effective management of these events requires comprehensive group intention mining, which encompasses intention detection and intention attribution. Knowledge graph inference facilitates the detection of group intention in crisis events. This is supported by the construction of crisis knowledge graphs, which organize crisis elements and inter-element relations into structured semantic information. This paper provides a comprehensive overview of the research about knowledge graph in social crisis management, focusing on three key areas: knowledge graph construction and inference, knowledge graph-based interpretable crisis attribution, and risk management. Specifically, the interpretable semantics in crisis knowledge graphs enables attribution of intention. To illustrate the significance of knowledge graphs in group intention mining, the COVID-19 and China–US game events are selected as two case studies. Finally, the paper proposes future research directions to solve the limitations of existing knowledge graph-related methods in social crises.

Multimodal emotion recognition comprehensively identifies the emotion contained in multimodal data by bridging the gaps between heterogeneous dataset. In recent years, multimodal emotion recognition methods have gained significant attention and been shown to surpass single-modal approaches. Most of the existing multimodal emotion analysis methods simply combine different modalities to improve the recognition capability of consistent emotion expressions across multimodal data. However, it remains challenging to recognize the right emotion when multiple modalities' contents carry inconsistent or even contradictory emotions. To solve this problem, we propose a novel image-text emotion recognition model named Multimodal Cross-Attention Bayesian Network(MCABN). The entire network exploits Bayesian theory to learn the distribution of its weight parameters, making optimization directions and results of parameters interpretable. What's more, the model leverages the consistency and complementarity between visual content and textual description to arrive at accurate decisions. Specifically, for each modality, multiple explainable features (color, texture, and shape feature in the image, while adjective, adverb, verb, noun, and negative feature in the text) and one unexplainable feature are fused as its feature representation to reinforce emotion-related features. Then two single-modal attention modules(Visual Attention Module and Textual Attention Module) capture the most discriminative features in a single image and text; Two cross-modal attention modules(Image-guided Text Attention Module and Text-guided Image Attention Module) extract the complementary and dominant features between two modalities by interactive learning. Finally, the outputs of four attention modules are integrated through intermediate fusion to predict the final emotion. The experimental results on the NVTD and MVSA-Multiple dataset indicate that the proposed MCABN outperforms state-of-the-art baselines by substantial margins.

In the current social environment, social crisis events occur frequently with significant impacts.Group intention mining through automatic technologies for managing social crises has gained extensive attention.This paper presents an overview of research on group intention mining in social crisis events, covering three areas: knowledge graph inference, intention attribution, and risk management.Knowledge graph inference facilitates the detection of group intention in crisis events.It is supported by the construction of crisis knowledge graphs, which organize crisis elements and inter-element relations into structured semantic knowledge.The interpretable semantics in the crisis knowledge graphs enables attribution of intention.Group intention mining consists of intention detection and intention attribution, serving the risk management of social crisis events.To gain insights into the process of group intention mining in social crises, the Covid-19 event is selected as a case study.Finally, the paper proposes future research directions to solve the limitations of existing intention mining methods in social crises.