There is little research on the degrees of drivers’ short-term behaviours regarding driving safety. To solve this problem, this paper investigated the concept of driving proneness and evaluated the propensities of different drivers to engage in different operations for the following scenarios of urban traffic. From the real driving data of sixteen drivers on a city road, car-following data fragments were extracted and six key parameters were obtained: throttle percentage, change rate of throttle percentage, brake pressure, change rate of brake pressure, absolute value of steering angle and absolute value of steering angle speed. Symbolic Aggregate Approximation was used to reduce the dimensionality of the parameters. The input of the Hidden Markov Model-Viterbi was obtained by the use of statistical methods. The output of the model is the probability of the three proneness states of introversion, neutrality and extroversion, from which the proneness value of each driver was calculated. The weighted proneness value of each driver was obtained by the use of the entropy weight method to assign weights to each parameter. The operating characteristics of the drivers were also analysed and described. The method presented in this paper can provide accurate and real-time warning in network-driven environments.

很少有关于驾驶员短期行车安全行为的研究。为了解决这个问题，本文研究了驾驶倾向性的概念，并针对以下城市交通情况评估了不同驾驶员从事不同操作的倾向。从城市道路上十六名驾驶员的实际驾驶数据中，提取出汽车跟随数据片段，并获得六个关键参数：节气门百分比，节气门百分比变化率，制动压力，制动压力变化率，转向角绝对值和转向角速度的绝对值。使用符号聚集近似来减少参数的维数。隐马尔可夫模型-维特比的输入是通过使用统计方法获得的。该模型的输出是内向，中立和外向这三个倾向状态的概率，从中可以计算出每个驾驶员的倾向值。通过使用熵权重方法为每个参数分配权重来获得每个驱动程序的加权倾向度值。还对驱动程序的操作特性进行了分析和描述。本文提出的方法可以在网络驱动的环境中提供准确和实时的警告。