Segmentation of driving areas for autonomous vehicle based on deep learning method using automotive camera sensor

被引：3
作者：
Lee S.-H. [1 ]
Kang Y. [2 ]
机构：
[1] Department of Secured Smart Electric Vehicle, Kookmin University
[2] Department of Automotive Engineering, Kookmin University
来源：
J. Inst. Control Rob. Syst. | 2020年 / 6卷 / 452-461期
关键词：
Autonomous driving; Berkeley Deep Drive; Deep learning; Driving area segmentation; Image recognition;
D O I：
10.5302/J.ICROS.2020.20.0016
中图分类号：
学科分类号：
摘要：
This paper employs a deep learning method for segmenting drivable and non-drivable areas in an urban environment using the BDD (Berkeley Deep Drive) 100K image database. In particular, we propose a semantic segmentation network using an encoder module based on depth-wise separable, non-bottleneck-ID, and pyramid pooling. The BDD 100K dataset provides reference classification results of directly drivable area, alternative drivable area, and none, under different weather, time, and location conditions. The performance of the developed network is verified using a computation environment equipped with a GPU (Graphics Processing Unit) for evaluating its feasibility of real-time implementation. © ICROS 2020.
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收藏
页码：452 / 461
页数：9
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