Driver behavioural classification from trajectory data

M. Rigolli, Q. Williams, M. Gooding, M. Brady
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引用次数: 17

Abstract

In recent years, traffic video surveillance has increased significantly. However, most of the footage is reviewed by humans or not at all. Tools capable of analysing traffic video sequences and autonomously extracting information are required. This paper presents an analysis of two automatic methods for classifying driver behaviour using only data provided by vehicle trackers. The algorithms are tested on several simulated traffic situations and their performance is compared to human observers. Factor analysis is shown to outperform human observers. We believe this is the first time automatic behavioural clustering of drivers using trajectory information has been successfully demonstrated.
基于轨迹数据的驾驶员行为分类
近年来,交通视频监控有了显著的增长。然而,大多数视频都是由人类审查的,或者根本就没有。需要能够分析交通视频序列并自动提取信息的工具。本文分析了两种仅使用车辆跟踪器提供的数据对驾驶员行为进行自动分类的方法。在几种模拟交通情况下对算法进行了测试,并与人类观察者进行了性能比较。因素分析被证明优于人类观察者。我们相信这是第一次使用轨迹信息成功地证明了驾驶员的自动行为聚类。
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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