The recent advancements in 3D object detection and distance estimation are significantly enhancing the capabilities of autonomous systems and advanced driver assistance systems (ADAS). Researchers are focusing on developing lightweight and efficient models that can operate in real-time on resource-constrained devices, particularly for applications like collision avoidance in mobile ADAS. Innovations in pose estimation and depth estimation are being integrated with object detection models to improve accuracy and reduce computational costs. Additionally, there is a growing emphasis on domain generalization and adaptation to ensure robust performance across various datasets and environments. Uncertainty estimation is also gaining traction as a critical component to enhance the reliability and safety of 3D object detection systems. Notably, the integration of prompt learning and probabilistic sampling mechanisms is showing promise in maintaining generalization while improving task-specific performance. Overall, the field is moving towards more efficient, accurate, and reliable solutions that can be deployed in real-world scenarios.