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首页> 外文期刊>IEEE Signal Processing Magazine >Driver-Behavior Modeling Using On-Road Driving Data: A new application for behavior signal processing
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Driver-Behavior Modeling Using On-Road Driving Data: A new application for behavior signal processing

机译：使用道路驾驶数据进行驾驶员行为建模：行为信号处理的新应用

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This article reviews data-centric approaches for statistical modeling of driver behavior. Modeling driver behavior is challenging due to its stochastic nature and the high degree of inter- and intradriver variability. One way to deal with the highly variable nature of driving behavior is to employ a data-centric approach that models driver behavior using large amounts of driving data collected from numerous drivers in a variety of traffic conditions. To obtain large amounts of realistic driving data, several projects have collected real-world driving data. Statistical machine-learning techniques, such as hidden Markov models (HMMs) and deep learning, have been successfully applied to model driver behavior using large amounts of driving data. We have also collected on-road data recording hundreds of drivers over more than 15 years. We have applied statistical signal processing and machine-learning techniques to this data to model various aspects of driver behavior, e.g., driver pedal-operation, car-following, and lane-change behaviors for predicting driver behavior and detecting risky driver behavior and driver frustration. By reviewing related studies and providing concrete examples of our own research, this article is intended to illustrate the usefulness of such data-centric approaches for statistical driver-behavior modeling.
机译：本文介绍了以数据为中心的驾驶员行为统计建模方法。由于驾驶员行为的随机性以及驾驶员之间和驾驶员内部的高度可变性，因此对驾驶员行为进行建模具有挑战性。处理驾驶行为的高度可变性质的一种方法是采用以数据为中心的方法，该方法使用从各种交通状况下从众多驾驶员处收集的大量驾驶数据来对驾驶员行为进行建模。为了获得大量逼真的驾驶数据，一些项目已经收集了现实驾驶数据。统计机器学习技术，例如隐马尔可夫模型（HMM）和深度学习，已成功地用于使用大量驾驶数据对驾驶员行为进行建模。我们还收集了超过15年的道路数据，记录了数百名驾驶员。我们已经对该数据应用了统计信号处理和机器学习技术，以对驾驶员行为的各个方面进行建模，例如驾驶员踏板操作，跟车和变道行为，以预测驾驶员行为并检测危险的驾驶员行为和驾驶员沮丧。通过回顾相关研究并提供我们自己研究的具体示例，本文旨在说明这种以数据为中心的方法对于统计驾驶员行为建模的有用性。

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