用于驾驶员行为预测的马尔可夫切换模型：智能手机上的用例
Markov Switching Model for Driver Behavior Prediction: Use cases on Smartphones
摘要：
一些智能交通系统专注于研究各种驾驶员行为的许多目标。这包括分析驾驶员动作、灵敏度、注意力分散和响应时间的能力。由于数据汇集是学习和验证不同驾驶情况的主要关注点之一，我们提出了一个驾驶员行为切换模型，通过使用智能手机的低成本数据汇集解决方案进行验证。使用真实数据集对所提出的模型进行验证，以预测驾驶员在短时间内的行为。提出了关于运动检测（特别是使用智能手机的驾驶行为检测）的文献调查。多个马尔可夫切换变量自回归（MSVAR）模型的实施，以实现一个复杂的拟合与收集的驾驶员行为数据。这不仅可以对驾驶员行为进行更准确的预测，还可以对整个驾驶情况进行更准确的预测。所提出的模型的性能，以及一个合适的模型选择标准。所提出的驾驶员行为预测框架可以潜在地用于事故预测和驾驶员安全系统。
英文摘要：
Several intelligent transportation systems focus on studying the various driver behaviors for numerous objectives. This includes the ability to analyze driver actions, sensitivity, distraction, and response time. As the data collection is one of the major concerns for learning and validating different driving situations, we present a driver behavior switching model validated by a low-cost data collection solution using smartphones. The proposed model is validated using a real dataset to predict the driver behavior in short duration periods. A literature survey on motion detection (specifically driving behavior detection using smartphones) is presented. Multiple Markov Switching Variable Auto-Regression (MSVAR) models are implemented to achieve a sophisticated fitting with the collected driver behavior data. This yields more accurate predictions not only for driver behavior but also for the entire driving situation. The performance of the presented models together with a suitable model selection criteria is also presented. The proposed driver behavior prediction framework can potentially be used in accident prediction and driver safety systems.
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