Department of Software, Konkuk University, Seoul 05029, Korea.
Department of Computer Science, Kangwon National University, Gangwon-do 24341, Korea.
Sensors (Basel). 2018 Oct 30;18(11):3686. doi: 10.3390/s18113686.
Automatically recognizing dangerous situations for a vehicle and quickly sharing this information with nearby vehicles is the most essential technology for road safety. In this paper, we propose a real-time deceleration pattern-based traffic risk detection system using smart mobile devices. Our system detects a dangerous situation through machine learning on the deceleration patterns of a driver by considering the vehicle's headway distance. In order to estimate the vehicle's headway distance, we introduce a practical vehicle detection method that exploits the shadows on the road and the taillights of the vehicle. For deceleration pattern analysis, the proposed system leverages three machine learning models: neural network, random forest, and clustering. Based on these learning models, we propose two types of decision models to make the final decisions on dangerous situations, and suggest three types of improvements to continuously enhance the traffic risk detection model. Finally, we analyze the accuracy of the proposed model based on actual driving data collected by driving on Seoul city roadways and the Gyeongbu expressway. We also propose an optimal solution for traffic risk detection by analyzing the performance between the proposed decision models and the improvement techniques.
自动识别车辆的危险情况，并迅速将此信息与附近的车辆共享，这是道路安全最关键的技术。在本文中，我们提出了一种基于智能移动设备的实时减速模式的交通风险检测系统。我们的系统通过考虑车辆的车头时距，通过对驾驶员减速模式进行机器学习来检测危险情况。为了估计车辆的车头时距，我们引入了一种实用的车辆检测方法，利用道路上的阴影和车辆的尾灯。对于减速模式分析，所提出的系统利用了三种机器学习模型：神经网络、随机森林和聚类。基于这些学习模型，我们提出了两种类型的决策模型，对危险情况做出最终决策，并提出了三种类型的改进方法，以不断增强交通风险检测模型。最后，我们根据在首尔市道路和京釜高速公路上行驶时收集的实际驾驶数据来分析所提出模型的准确性。我们还通过分析所提出的决策模型和改进技术之间的性能，提出了一种交通风险检测的最佳解决方案。
