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

By Eduardo Arnold, Omar Y. Al-Jarrah, Mehrdad Dianati, Saber Fallah, David Oxtoby, A. Mouzakitis. IEEE transactions on intelligent transportation systems (Print), 2019

661 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d25c6946c6a7657e51480fd441ab32f5eaf1654c

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Citations in the corpus

Cooperative Perception for 3D Object Detection in Driving Scenarios Using Infrastructure Sensors (2019, background): “A dedicated comprehensive review of 3D object detection techniques, covering all categories, can be found in [5], [13].”
Multimodal End-to-End Autonomous Driving (2019, background): “For more details on multimodal object detection, the reader is referred to recent surveys [100], [97].”
Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning (2020, neutral): “on is of fundamental importance, as failing to detect road participants and obstacles might lead to serious incidents. 3D object detection for autonomous driving is an extremely popular research area [40] with new algorithms proposed everyday. The environment model obtained in our method actually ha”
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, neutral): “[45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], scene understanding [68], [69], [70], tracking [71], [72],”
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2019, background): “[10] survey the 3D object detection problem in autonomous driving.”
3D Object Detection for Autonomous Driving: A Comprehensive Survey (2022, result): “Compared to the existing surveys (Arnold et al., 2019; Liang et al., 2021b; Qian et al., 2021b), our paper broadly covers the recent advances in this area, e.”
3D Object Detection for Autonomous Driving: A Survey (2021, methodology): “As of this main text in June 2021, we notice that rather a few investigations [13, 35, 14, 36] have looked into this application issue.”
A Survey of Deep Learning-Based Object Detection (2019, background): “Please refer to a recently published survey [180] for more details.”
ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation (2023, background): “, autonomous driving [1, 29]) operate in non-stationary and constantly changing environments, which contain heterogeneous and dynamic domain distribution shifts.”
Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review (2020, background): “re, it is common to train a model for multiple tasks [45]. In an autonomous vehicle, the object detection module exploits raw sensor data, and it usually relies on a model with millions of parameters [46]. Thus, it can be a good candidate for parameter sharing with the behaviour prediction module. L”
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review (2020, methodology): “For 3D object localization and detection task, the most frequently used metrics are: Average Precision (AP3D) [66], and Average Orientation Similarity (AOS) [36].”