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3D Object Detection in Autonomous Driving

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Autonomous Driving Perception

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

3D object detection is an important perception module in autonomous driving systems. It recognizes sensor observations and predicts locations, sizes and orientations of key objects, which provides both semantic and spatial information for high-level decision making. In this chapter, we first introduce and analyze the properties of commonly used perceptual sensors in autonomous vehicles: cameras, LiDARs and RADARs. Then we define the research problem, detail the assumptions and introduce evaluation metrics of 3D object detection in the context of autonomous driving. The main body reviews the state-of-the-art techniques and categorize them into camera-based, LiDAR-based, RADAR-based and multi-sensor fusion methods. For each method, we point out the main problems and their existing solutions. By analyzing the limitations of existing methods, we propose promising directions and open problems for future research.

Peng Yun, Yuxuan Liu, Xiaoyang Yan—equal contribution.

Rui Fan, Ming Liu—co-corresponding author.

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Notes

  1. 1.

    It is noted that, different from the method calculating with intersection over union in the KITTI dataset, nuScenes calculate the \(AP_{BEV}\) with the 2D center distance on the ground plane.

  2. 2.

    If a point cloud is partitioned into a [10,400,352] dense grid, only around 5300 voxels are non-empty.

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Authors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    Peng Yun, Yuxuan Liu, Xiaoyang Yan, Na Jin & Ming Liu

  2. Tongji University, Shanghai, China

    Jiahang Li & Rui Fan

  3. Jilin University, Changchun, China

    Jiachen Wang

  4. Huawei Autonomous Driving Solutions, Shanghai, China

    Lei Tai

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  1. Peng Yun
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Correspondence to Peng Yun .

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Editors and Affiliations

  1. Control Science & Engineering, Tongji University and Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China

    Rui Fan

  2. Control Science & Engineering, Tongji University and Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China

    Sicen Guo

  3. Electrical & Electronic Engineering, University of Bristol, Bristol, UK

    Mohammud Junaid Bocus

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Yun, P. et al. (2023). 3D Object Detection in Autonomous Driving. In: Fan, R., Guo, S., Bocus, M.J. (eds) Autonomous Driving Perception. Advances in Computer Vision and Pattern Recognition. Springer, Singapore. https://doi.org/10.1007/978-981-99-4287-9_5

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