Driving situation recognition with uncertainty management and rule-based systems

被引：13
|
作者：
Nigro, JM
Loriette-Rougegrez, S
Rombaut, M
机构：
[1] Univ Technol Troyes, Lab LM2S, F-10010 Troyes, France
[2] Inst Natl Polytech Grenoble, LIS IUT Grenoble, F-38031 Grenoble, France
来源：
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2002年 / 15卷 / 3-4期
关键词：
situation recognition; evidence theory; artificial intelligence;
D O I：
10.1016/S0952-1976(02)00070-2
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The recognition of a temporal sequence is a complex problem, especially in the framework of driving situations. However, this recognition is essential for the development of driving assistance systems. This paper presents a rule-based system that manages the real-time measurements got from sensors of an experimental vehicle, in order to determine the current possible maneuvers worked out by the driver. The particularity of the proposed system is that it manages the inaccuracy of the data and the uncertainty of the recognition, using fuzzy subsets and beliefs on hypotheses. (C) 2002 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码：217 / 228
页数：12
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