Sensing, Perception and Decision for Deep Learning Based Autonomous Driving

被引：1
作者：
Yamashita, Takayoshi [1 ]
机构：
[1] Chubu Univ, 1200 Matsumoto Cho, Kasugai, Aichi, Japan
来源：
DISTRIBUTED, AMBIENT AND PERVASIVE INTERACTIONS: TECHNOLOGIES AND CONTEXTS, DAPI 2018, PT II | 2018年 / 10922卷
关键词：
Deep learning; Object detection; Semantic segmentation; CNN; FEATURES;
D O I：
10.1007/978-3-319-91131-1_12
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Toward the realization of autonomous driving, deep learning has attracted the most attention, and it is seen as indispensable technology. AlexNet which consists of eight layers and incorporating ideas for improving generalization, was able to accomplish substantial improvement in accuracy with image recognition task. Since then, not only image recognition but also various tasks and applications to various fields are dramatically advanced. Even in the autonomous driving field, many manufacturers have taken aggressive efforts and are pushing ahead with practical application. In this paper, we will introduce the tasks being tackled for autonomous driving. The tasks introduced here are object detection, human pose estimation, and semantic segmentation from images and other sensors. By combining these methods, it is possible to realize safer automatic operation system.
引用
收藏
页码：152 / 163
页数：12
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