A Survey on 3D Object Detection Methods for Autonomous Driving Applications

• Published in: IEEE transactions on intelligent transportation systems Vol. 20; no. 10; pp. 3782 - 3795
• Main Authors: Arnold, Eduardo, Al-Jarrah, Omar Y., Dianati, Mehrdad, Fallah, Saber, Oxtoby, David, Mouzakitis, Alex
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-10-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous vehicles; Cameras; Collision avoidance; computer vision; deep learning; Identification methods; intelligent vehicles; Land mines; Laser radar; Machine learning; Object detection; Object recognition; Path planning; Perception; Product development; Sensors; Three-dimensional displays; Two dimensional displays
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2019.2892405

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, which has been tackled by several works, most of them using 2D detection methods. However, the 2D methods do not provide depth information, which is required for driving tasks, such as path planning, collision avoidance, and so on. Alternatively, the 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 the 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 the research gaps and future research directions.