Abstract

IVIS (In-vehicle Information System) is an important factor causing driver distraction. To study the driver distraction detection method when operating IVIS, the effectiveness of driving performance indicators in the identification of driving distraction was verified by the method of variance analysis. Forty participants were selected to conduct the driver distraction experiment when operating IVIS, and the data of driving performance indicators such as eye movement, speed, et al. were obtained. According to the driving performance data of IVIS, a real-time detection of distraction based on driving performance was built by using support vector machine, and three kernel functions of the model were conducted comparative analysis and validation. The results show that SVM models can effectively evaluate the degree of drivers’ distraction. At the same time, when the Radial Basis Function is used as a kernel function, the accuracy for recognizing driver distraction is 89.9%, which is higher than when the sigmoid polynomial kernel function and SAVE-IT model are used. The research could be applied in the design of adaptive in-vehicle systems and the evaluation of driving distraction, providing theoretical support and reference for the development of vehicle-mounted information systems and the management of driver distraction prevention measures.

• Highlights

• We verify the effectiveness of driving performance indicators as decision variables for driving distraction.

• We use driving performance indicators as inputs to a distraction detection algorithm.

• We examine changes in the level of information about driving distraction.

• SVMs method can be used to detect driving distraction during IVIS operations in real time.

• The accuracy for using Radial Basis Function to recognizing the driver’s level of distraction is higher than the Radial Basis Function.

摘要

IVIS（车载信息系统）是导致驾驶员分心的重要因素。为研究操作IVIS时的驾驶员分心检测方法，通过方差分析的方法验证了驾驶性能指标在识别驾驶分心中的有效性。选取40名被试进行IVIS操作时的驾驶员分心实验，获取眼球运动、速度等驾驶性能指标数据。获得。根据IVIS的驾驶性能数据，利用支持向量机构建了基于驾驶性能的分心实时检测，并对模型的三个核函数进行了对比分析和验证。结果表明，支持向量机模型可以有效地评估驾驶员的分心程度。同时，当使用径向基函数作为核函数时，识别驾驶员分心的准确率为89.9%，高于使用sigmoid多项式核函数和SAVE-IT模型时的准确率。该研究可应用于自适应车载系统的设计和驾驶分心的评估，为车载信息系统的开发和驾驶员分心预防措施的管理提供理论支持和参考。

• 强调

• 我们验证了驾驶性能指标作为驾驶分心决策变量的有效性。

• 我们使用驾驶性能指标作为分心检测算法的输入。

• 我们研究了有关驾驶分心的信息水平的变化。

• 支持向量机方法可用于实时检测 IVIS 操作期间的驾驶分心。

• 使用径向基函数识别驾驶员分心程度的准确性高于径向基函数。