This paper presents a comprehensive review of 3D object detection methods in autonomous driving, emphasizing robustness alongside accuracy and latency. The study evaluates camera-only, LiDAR-only, and multi-modal approaches, focusing on their trade-offs in accuracy, latency, and robustness across datasets like KITTI-C and nuScenes-C. Multi-modal methods are found to be more robust, and a novel taxonomy is introduced to organize the literature. The paper highlights the importance of robustness in real-world scenarios, where environmental variability, sensor noise, and misalignment can significantly impact performance. It discusses three key factors affecting detection robustness: environmental variability, sensor noise, and misalignment. The paper also presents a detailed analysis of various 3D object detection methods, including monocular, stereo-based, and multi-view approaches, and evaluates their performance in terms of accuracy, latency, and robustness. The study concludes that multi-modal methods offer superior robustness and that future research should focus on improving robustness while maintaining accuracy and efficiency. The paper also discusses the challenges and limitations of current methods and provides insights for future research in autonomous driving.This paper presents a comprehensive review of 3D object detection methods in autonomous driving, emphasizing robustness alongside accuracy and latency. The study evaluates camera-only, LiDAR-only, and multi-modal approaches, focusing on their trade-offs in accuracy, latency, and robustness across datasets like KITTI-C and nuScenes-C. Multi-modal methods are found to be more robust, and a novel taxonomy is introduced to organize the literature. The paper highlights the importance of robustness in real-world scenarios, where environmental variability, sensor noise, and misalignment can significantly impact performance. It discusses three key factors affecting detection robustness: environmental variability, sensor noise, and misalignment. The paper also presents a detailed analysis of various 3D object detection methods, including monocular, stereo-based, and multi-view approaches, and evaluates their performance in terms of accuracy, latency, and robustness. The study concludes that multi-modal methods offer superior robustness and that future research should focus on improving robustness while maintaining accuracy and efficiency. The paper also discusses the challenges and limitations of current methods and provides insights for future research in autonomous driving.