This paper reviews the advancements and challenges in 3D object detection for autonomous driving, emphasizing the importance of robustness alongside accuracy and latency. The study systematically examines camera-only, LiDAR-only, and multi-modal 3D object detection algorithms, evaluating their performance on datasets like KITTI-C and nuScenes-C. Multi-modal approaches are found to exhibit superior robustness, and a new taxonomy is introduced to enhance clarity in the literature. The paper highlights the need for robustness in real-world scenarios, where environmental variability, sensor noise, and misalignment can significantly impact detection performance. It provides practical guidance for future research and deployment, focusing on accuracy, latency, and robustness. The review also discusses the limitations of monocular and stereo-based methods, and the advantages of multi-view (bird's-eye view) approaches. The analysis reveals that while monocular methods are faster, they lack precision, and stereo and multi-view methods are more accurate but slower. The paper concludes by emphasizing the importance of balancing latency and accuracy to meet the dual requirements of real-time responsiveness and safety in autonomous driving.This paper reviews the advancements and challenges in 3D object detection for autonomous driving, emphasizing the importance of robustness alongside accuracy and latency. The study systematically examines camera-only, LiDAR-only, and multi-modal 3D object detection algorithms, evaluating their performance on datasets like KITTI-C and nuScenes-C. Multi-modal approaches are found to exhibit superior robustness, and a new taxonomy is introduced to enhance clarity in the literature. The paper highlights the need for robustness in real-world scenarios, where environmental variability, sensor noise, and misalignment can significantly impact detection performance. It provides practical guidance for future research and deployment, focusing on accuracy, latency, and robustness. The review also discusses the limitations of monocular and stereo-based methods, and the advantages of multi-view (bird's-eye view) approaches. The analysis reveals that while monocular methods are faster, they lack precision, and stereo and multi-view methods are more accurate but slower. The paper concludes by emphasizing the importance of balancing latency and accuracy to meet the dual requirements of real-time responsiveness and safety in autonomous driving.