The recent advancements in multi-modal 3D tracking and localization systems have significantly enhanced the robustness and accuracy of autonomous systems. Researchers are increasingly focusing on integrating complementary sensor data, such as LiDAR and camera inputs, to overcome the limitations of single-modal approaches. This integration is leading to the development of more sophisticated two-stage tracking paradigms that refine initial trajectory estimates through cross-modal correction, thereby improving overall tracking performance. Additionally, the use of virtual cues generated from multimodal data is proving to be effective in addressing sparsity issues in point cloud data, which is crucial for accurate 3D object tracking. In the realm of localization, predictive risk assessment techniques are being employed to preemptively identify and mitigate potential alignment failures, particularly in environments with limited geometric features. These innovations are not only advancing the state-of-the-art but also paving the way for more reliable and resilient autonomous systems. Notably, the introduction of two-stage tracking frameworks and predictive risk assessment methods are particularly groundbreaking, offering substantial improvements in performance and robustness.