LiDAR Point Cloud Augmentation for Adverse Conditions Using Conditional Generative Model

Author:

Zhang Yuxiao1ORCID,Ding Ming2,Yang Hanting1ORCID,Niu Yingjie1ORCID,Ge Maoning1,Ohtani Kento1,Zhang Chi3ORCID,Takeda Kazuya14

Affiliation:

1. Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-Ward, Nagoya 464-8601, Japan

2. Zhejiang Fubang Technology Inc., Ningbo R&D Campus Block A, Ningbo 315048, China

3. School of Materials Science and Engineering, Liaoning University of Technology, No. 169, Shiying St., Guta District, Jinzhou 121001, China

4. Tier IV Inc., Nagoya University Open Innovation Center, 1-3, Mei-eki 1-chome, Nakamura-Ward, Nagoya 450-6610, Japan

Abstract

The perception systems of autonomous vehicles face significant challenges under adverse conditions, with issues such as obscured objects and false detections due to environmental noise. Traditional approaches, which typically focus on noise removal, often fall short in such scenarios. Addressing the lack of diverse adverse weather data in existing automotive datasets, we propose a novel data augmentation method that integrates realistically simulated adverse weather effects into clear condition datasets. This method not only addresses the scarcity of data but also effectively bridges domain gaps between different driving environments. Our approach centers on a conditional generative model that uses segmentation maps as a guiding mechanism to ensure the authentic generation of adverse effects, which greatly enhances the robustness of perception and object detection systems in autonomous vehicles, operating under varied and challenging conditions. Besides the capability of accurately and naturally recreating over 90% of the adverse effects, we demonstrate that this model significantly improves the performance and accuracy of deep learning algorithms for autonomous driving, particularly in adverse weather scenarios. In the experiments employing our augmented approach, we achieved a 2.46% raise in the 3D average precision, a marked enhancement in detection accuracy and system reliability, substantiating the model’s efficacy with quantifiable improvements in 3D object detection compared to models without augmentation. This work not only serves as an enhancement of autonomous vehicle perception systems under adverse conditions but also marked an advancement in deep learning models in adverse condition research.

Funder

Liaoning University of Technology

Publisher

MDPI AG

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