The recent advancements in multimodal object detection and tracking have significantly pushed the boundaries of what is possible in autonomous driving and 3D object detection. A notable trend is the shift towards late fusion techniques, which integrate information from multiple modalities at the decision level, avoiding the pitfalls of early and deep fusion methods. This approach not only enhances the robustness and accuracy of detection but also allows for more seamless integration of diverse data sources. Additionally, there is a growing emphasis on uncertainty estimation and transparency in model predictions, which is crucial for building trust in autonomous systems. The integration of contrastive learning and dual attention mechanisms in Transformer-based models has shown promising results in improving feature extraction and fusion across different modalities. Furthermore, the development of self-supervised and test-time optimization methods for fluid motion estimation and scene flow estimation demonstrates a move towards more efficient and data-light solutions, which are essential for real-world applications. Notably, the introduction of uncertainty-aware sensor fusion frameworks and the exploration of unaligned multimodal data handling are paving the way for more versatile and practical systems. These innovations collectively indicate a field that is rapidly evolving towards more sophisticated, reliable, and adaptable solutions for complex detection and tracking tasks.