Incomplete vehicle information completion using generative adversarial network to enhance the safety of autonomous driving

Jingzhi Tu; Gang Mei; Francesco Piccialli

doi:10.1117/12.2624136

19 December 2021 Incomplete vehicle information completion using generative adversarial network to enhance the safety of autonomous driving

Jingzhi Tu, Gang Mei, Francesco Piccialli

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Proceedings Volume 12128, Second International Conference on Industrial IoT, Big Data, and Supply Chain; 121280B (2021) https://doi.org/10.1117/12.2624136
Event: 2nd International Conference on Industrial IoT, Big Data, and Supply Chain, 2021, Macao, China

Abstract

Autonomous driving is the key technology of intelligent logistics in Industrial Internet of Things (IIoT). 3D vision using Light Detection And Ranging (LiDAR) under vehicle industrial standard is the rigid demand in autonomous driving due to its lower cost, more robust, richer information, and meeting the mass-production standards. However, the appearance of incomplete point clouds losing geometric and semantic information is inevitable owing to limitations of occlusion, sensor resolution, and viewing angle when the LiDAR is applied. The emergence of incomplete point clouds, especially incomplete vehicle point clouds, would lead to the reduction of the accuracy of autonomous driving vehicles in object detection, traffic alert, and collision avoidance. Therefore, the point fractal network (PF-Net), a precise and high-fidelity 3D point cloud repair network based on Generative Adversarial Network (GAN), is first applied to repair incomplete vehicle point clouds in autonomous driving. To evaluate the performance of the GAN-based point cloud repair network, an autonomous driving scene is created, where three incomplete vehicle point clouds are set for different autonomous driving situations. Experimental results demonstrate the effectiveness of the PF-Net for challenging vehicle point cloud completion tasks in autonomous driving.

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Jingzhi Tu, Gang Mei, and Francesco Piccialli "Incomplete vehicle information completion using generative adversarial network to enhance the safety of autonomous driving", Proc. SPIE 12128, Second International Conference on Industrial IoT, Big Data, and Supply Chain, 121280B (19 December 2021); https://doi.org/10.1117/12.2624136

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KEYWORDS

Clouds

LIDAR

Safety

Gallium nitride

3D vision

Standards development

Unmanned vehicles

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