With the development of modern society, the traffic environment is becoming increasingly complex. The global traffic accident rate remains high, and most of which are caused by the human factors of drivers. Intelligent vehicles (IVs) can reduce the man-caused accidents and effectively improve the traffic safety. The environment perception of IVs is one of the key technical problems to be solved. Leveraging the 2D images and 3D point clouds, this paper studies the environment perception of IVs step by step (from region-level to pixel-level and from 2D to 3D). Specifically, it focuses on three tasks: traffic objects perception, 2D environment perception, and 3D environment perception. Three well-performed methods are proposed in this paper, which fully exploit the semantic information.
The main research works are listed as follow:
(1) Focusing on the traffic object perception, a foreground segmentation approach for anchor-free object detection is proposed, which could alleviate the background influences under the complex traffic environment with little additional computation cost. On the basis of that, a novel traffic object detection network with foreground attention, called FII-CenterNet, is developed. To realize supervised learning without additional labeling cost, the foreground segmentation labels are generated based on the input bounding-box labels, and a multi-tasks loss function is designed. Extensive experimental results show that the proposed method can effectively segment the foreground and alleviate the background interference for the traffic object detection. Benefit from that, FII-CenterNet achieves good traffic object perception performance in both accuracy and efficiency.
(2) Focusing on the 2D environment perception, a semi-supervised semantic segmentation approach based on the conservative-progressive collaborative learning (CPCL) is proposed, which could reduce the huge cost of pixel-level labeling. Inspired by the idea of “seeking common ground while reserving differences”, the CPCL is proposed, and achieves the collaboration of conservative evolution and progressive exploration. To generate the pseudo labels for the disagreement region, a pseudo labeling method based on the class-wise disagreement indicator is proposed, which is from a macro point of view instead of focusing on the exact pixel. Besides, an adaptive dynamic loss function based on the predictive confidence is designed to deal with the noisy pseudo labels. Extensive experimental results show that the proposed method can effectively mine the unlabeled data, and perform well with only few labeled data. Benefit from that, the training cost of the model for the 2D environment perception can be effectively reduced without performance degradation.
(3) Focusing on the 3D environment perception, a large-scale point cloud segmentation approach based on the spatial contextual features learning is proposed, which could make full use of the rich geometric information provided by the point clouds. Specifically, a systematic method for learning the spatial contextual feature is proposed, including: the local spatial contextual information representation method based on the local direction, the local spatial contextual feature learning method based on the dual-distance, and the global spatial contextual feature learning method based on the relative volume ratio. On the basis of that, a corresponding module for spatial contextual learning is designed, and then a large-scale point clouds segmentation network, called SCF-Net, is developed. Extensive experimental results show that the proposed method can improve the 3D environment perception performance for the intelligent vehicle. Additionally, it can also be extended to the indoor environment perception with good performance for the mobile robot.
The research on semantic segmentation for environment perception of intelligent vehicles can make it understand the surroundings better, and provide more detailed and reliable information for the decision-planning system, which can improve the safety of IVs and further promote the development of both the technology and the industry.
|Keyword||图像语义分割 点云语义分割 半监督学习 环境感知 智能车|
|范嗣祺. 面向智能车环境感知的语义分割及其应用研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2022.|
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