An Autonomous Vehicle (AV) requires an accurate perception of its surrounding environment to operate reliably. The perception system of an AV, which normally employs machine learning (e.g., deep learning), transforms sensory data into semantic information that enables autonomous driving. Object detection is a fundamental function of this perception system that has been tackled by several works, most of which use 2D detection methods. However, 2D methods do not provide depth information, which

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... nformation, which is required for driving tasks, such as path planning, collision avoidance, etc. Alternatively, 3D object detection methods introduce a third dimension that reveals more detailed object's size and location information. Nonetheless, the detection accuracy of such methods needs to be improved. To the best of our knowledge this is the first survey on 3D object detection methods used for autonomous driving applications. This paper presents an overview of 3D object detection methods and prevalently used sensors and datasets in AVs. It then discusses and categorizes recent works based on sensors modalities into monocular, point cloud-based and fusion methods. We then summarize the results of the surveyed works and identify research gaps and future research directions.