不同道路线形下驾驶人认知分散状态监测

Driver cognitive distraction detection in different road lines

通过驾驶模拟实验采集了不同驾驶人在不同道路线形下的驾驶行为参数,通过对参数的统计分析,确立了表征正常驾驶和认知分散状态下驾驶的特征参数组。利用提取的特征参数组作为支持向量机模型输入,建立了不同驾驶人在不同道路线形下的认知分散状态监测模型。实验结果表明,在不同道路线形下分别进行监测的准确度(直道88.58%,弯道81.25%)高于采用同一模型不区分道路线形直接进行监测的准确度74.17%。研究同时表明个人驾驶习惯对驾驶人意识监测结果有重要影响。

Through driving simulator experiments, the original performance data of different drivers on different road lines were collected, from which the characteristic parameters were extracted using statistical analysis. Then cognitive distraction detection models were developed based on the support vector machine in view of different road lines. The characteristic parameters were used as the input of the models. The experiment results show that, using the proposed models, the detection accuracy is 88.58% for straight road, and 81.25% for curve road. The performance of the proposed models is better than that of the universal model, using which the detection accuracy is only 74. 17%. Experiment results also show that the cognitive distraction detection is also influenced by the driving styles of the individual drivers.