On-line recognition of critical driving situations

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
S. Jozová, Jaromír Tobiška, I. Nagy
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引用次数: 1

Abstract

According to the statistics about vehicle accidents, there are many causes such as traffic violations, reduced concentration, micro sleep, hasty aggression, but the most frequent cause of accidents at highways is a carelessness of the driver and violation of keeping a safe distance. Producers of vehicles try to take into account this situation by development of assistance systems which are able to avoid accidents or at least to mitigate its consequences. This urgent situation leaded to the described project of investigation of behavior of drivers in dangerous situations occurring in vehicle driving. The research is to help in solution of the present unsatisfactory situation in driving accidents. The developed decisionmaking algorithm of detection serious driving situations that can lead to accidents was tested in the laboratory of driving simulators in FTS CTU, Prague. The data for its testing resembled highway traffic.
关键驾驶情况的在线识别
根据对交通事故的统计,造成交通事故的原因有很多,如交通违规、注意力不集中、微睡眠、仓促攻击等,但高速公路上发生事故最常见的原因是驾驶员的粗心大意和违反安全距离。汽车制造商试图通过开发能够避免事故或至少减轻其后果的辅助系统来考虑这种情况。这种紧急情况导致了所描述的对车辆驾驶中发生的危险情况下驾驶员行为的调查项目。该研究有助于解决目前我国交通事故频发的现状。开发的检测可能导致事故的严重驾驶情况的决策算法在布拉格FTS CTU的驾驶模拟器实验室进行了测试。测试的数据类似于公路交通。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neural Network World
Neural Network World 工程技术-计算机:人工智能
CiteScore
1.80
自引率
0.00%
发文量
0
审稿时长
12 months
期刊介绍: Neural Network World is a bimonthly journal providing the latest developments in the field of informatics with attention mainly devoted to the problems of: brain science, theory and applications of neural networks (both artificial and natural), fuzzy-neural systems, methods and applications of evolutionary algorithms, methods of parallel and mass-parallel computing, problems of soft-computing, methods of artificial intelligence.
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