Abstract:

To explore the influencing factors of driving attention demand in urban roads, scenarios with various traffic variables were built. And 30 participants were tested on driving simulators for vision occlusion. The driving attention demand level is expressed by the vision occlusion probability. Stepwise logistic regression method is used to establish driving attentional demand level, and the impact of each variable is analyzed. The results show that the occlusion probability fluctuates greatly along the driving route. Different traffic variables require different amounts of driving attention. Specifically, intersections, bus stops, roadside parking cars, headway, meeting distance and road curvature are the most significant factors affecting driving attentional demand. The occlusion probability becomes lower when close to an intersection, and there is almost no occlusion after reaching the intersection. It indicates that drivers actively adapt to the attention demand of driving scenario, selectively obtain traffic variable information related to current driving, and predict the development trend of each traffic variable. The results can help improve the scenario sensitivity of driving distraction warning system.

Key words: traffic engineering, driving attentional demand, visual occlusion, logistic regression, driving simulator test

摘要：

为探索城市道路场景下影响驾驶注意力需求的交通因素，搭建包含多种交通变量的城市道路交通场景，对30位被试进行驾驶模拟器视线遮挡实验。以视线遮挡概率表示驾驶注意力需求等级，采用逐步逻辑回归方法建立各交通变量对驾驶注意力需求等级模型，并分析其影响方式。结果显示，遮挡概率沿驾驶路线波动较大，不同交通变量对驾驶注意力的需求不同。十字路口、公交车站、路边停放车辆、跟车距离、会车距离和弯道曲率等对驾驶注意力需求影响显著。越接近十字路口遮挡概率越小，到达十字路口时几乎没有遮挡。说明被试会主动适应外界交通环境对注意力的需求，有选择的获取与驾驶有关的交通变量信息，并预测各个交通变量的发展趋势。研究结果有助于提高驾驶分心预警系统的环境敏感性。

关键词: 交通工程, 驾驶注意力需求, 视线遮挡, 逻辑回归, 驾驶模拟实验