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基于高德导航数据的驾驶倾向性实时识别系统。

A Real-Time Recognition System of Driving Propensity Based on AutoNavi Navigation Data.

机构信息

College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China.

Collaborative Innovation Center for Intelligent Green Manufacturing Technology and Equipment of Shandong, Qingdao 266000, China.

出版信息

Sensors (Basel). 2022 Jun 28;22(13):4883. doi: 10.3390/s22134883.

DOI:10.3390/s22134883
PMID:35808374
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9269833/
Abstract

Driving propensity is the driver's attitude towards the actual traffic situation and the corresponding decision-making or behavior during the driving process. It is of great significance to improve the accuracy of safety early warning and reduce traffic accidents. In this paper, a real-time identification system of driving propensity based on AutoNavi navigation data is proposed. The main work includes: (1) A dynamic data acquisition method of AutoNavi navigation is proposed to obtain the time, speed and acceleration of the driver during the navigation process. (2) The dynamic data collection method of AutoNavi navigation is analyzed and verified through the dynamic data obtained in the real vehicle experiment. The principal component analysis method is used to process the experimental data to extract the driving propensity characteristics variables. (3) The fruit fly optimization algorithm combined with GRNN (generalized neural network) and the feature variable set are used to build a FOA-GRNN-based model. The results show that the overall accuracy of the model can reach 94.17%. (4) A driving propensity identification system is constructed. The system has been verified through real vehicle test experiments. This paper provides a novel and convenient method for building personalized intelligent driver assistance systems in practical applications.

摘要

驾驶倾向是驾驶员对实际交通状况的态度以及在驾驶过程中的相应决策或行为。提高安全预警的准确性，减少交通事故具有重要意义。本文提出了一种基于 AutoNavi 导航数据的驾驶倾向实时识别系统。主要工作包括：（1）提出了一种 AutoNavi 导航动态数据采集方法，以获取驾驶员在导航过程中的时间、速度和加速度。（2）通过在实车实验中获得的动态数据对动态数据采集方法进行了分析和验证。采用主成分分析法对实验数据进行处理，提取出驾驶倾向特征变量。（3）利用果蝇优化算法（fruit fly optimization algorithm，FOA）与广义神经网络（generalized neural network，GRNN）相结合并结合特征变量集，构建了基于 FOA-GRNN 的模型。结果表明，该模型的整体准确率可达 94.17%。（4）构建了驾驶倾向识别系统。该系统通过实车测试实验进行了验证。本文为构建个性化智能驾驶辅助系统在实际应用中提供了一种新颖、便捷的方法。

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