Every 24 seconds,someone dies on the road due to road accidents and it is the 8th leading cause of death and the first among children aged 15–29 years.1.35 million people globally die every year due to road traffic c...Every 24 seconds,someone dies on the road due to road accidents and it is the 8th leading cause of death and the first among children aged 15–29 years.1.35 million people globally die every year due to road traffic crashes.An additional 20–50 million suffer from non-fatal injuries,often resulting in longterm disabilities.This costs around 3%of Gross Domestic Product to most countries,and it is a considerable economic loss.The governments have taken various measures such as better road infrastructures and strict enforcement of motor-vehicle laws to reduce these accidents.However,there is still no remarkable reduction in the number of accidents.To ensure driver safety and achieve vision of zero accidents,there is a great need to monitor drivers’driving styles.Most of the existing driving behavior monitoring solutions are based on expensive hardware sensors.As most people are using smartphones in the modern era,a system based on mobile application is proposed,which can reduce the cost for developing intelligent transport systems(ITS)to a large extent.In this paper,we utilize the accelerometer sensor data and the global positioning system(GPS)sensor deployed in smartphones to recognize driving and speeding events.A driving style recognition system based on fuzzy logic is designed to classify different driving styles and control reckless driving by taking the longitudinal/lateral acceleration and speed as input parameters.Thus,the proposed system uses fuzzy logic rather than taking the crisp values of the sensors.Results indicate that the proposed system can classify reckless driving based on fuzzy logic and,therefore,reduce the number of accidents.展开更多
A gear position decision method used in automated mechanical transmission is introduced. The algorithm of the mechod is composed of a driving environment and driver's intention estimator, the shift schedules suit ...A gear position decision method used in automated mechanical transmission is introduced. The algorithm of the mechod is composed of a driving environment and driver's intention estimator, the shift schedules suit for each typical driving environment and driver's intention situation, and an inference ligic to determine the most proper gear position for the present situation. The estimator identifies the driving environment and driver's intention features which are divided into some typical models. Based on the identified results, the algorithm works out the best gear position. It just simulates the course of driver's making gear position decision when driving a automobile with manual transmission. The test results show that the automated mechanical transmission with the method gives less unnecessary shifting and more proper gear position than common shift schedules.展开更多
在室内定位系统中,基于Wi-Fi技术的定位精度很大程度上依赖于信号的稳定,信号的多径效应与非视距(Non Line of Sight,NLOS)会增大定位误差。行人航位推算(Pedestrian Dead Reckoning,PDR)定位系统会因传感器自身误差与噪声产生累计误差...在室内定位系统中,基于Wi-Fi技术的定位精度很大程度上依赖于信号的稳定,信号的多径效应与非视距(Non Line of Sight,NLOS)会增大定位误差。行人航位推算(Pedestrian Dead Reckoning,PDR)定位系统会因传感器自身误差与噪声产生累计误差。针对上述问题,提出了一种改进的PDR与最小一乘法(Least Absolute Deviation,LAD)融合的室内定位算法。该算法基于模糊逻辑将PDR算法的步长固定参数改进为变量参数,同时根据LAD的定位结果对PDR进行周期性位置与拐点位置校正,选择扩展卡尔曼滤波(Extend Kalman Filter,EKF)将改进的PDR与LAD进行融合,以降低PDR的累计误差与LAD的突变误差,提高定位精度。实验结果表明:所提方法较其他方法具有更高的定位精度。展开更多
文摘Every 24 seconds,someone dies on the road due to road accidents and it is the 8th leading cause of death and the first among children aged 15–29 years.1.35 million people globally die every year due to road traffic crashes.An additional 20–50 million suffer from non-fatal injuries,often resulting in longterm disabilities.This costs around 3%of Gross Domestic Product to most countries,and it is a considerable economic loss.The governments have taken various measures such as better road infrastructures and strict enforcement of motor-vehicle laws to reduce these accidents.However,there is still no remarkable reduction in the number of accidents.To ensure driver safety and achieve vision of zero accidents,there is a great need to monitor drivers’driving styles.Most of the existing driving behavior monitoring solutions are based on expensive hardware sensors.As most people are using smartphones in the modern era,a system based on mobile application is proposed,which can reduce the cost for developing intelligent transport systems(ITS)to a large extent.In this paper,we utilize the accelerometer sensor data and the global positioning system(GPS)sensor deployed in smartphones to recognize driving and speeding events.A driving style recognition system based on fuzzy logic is designed to classify different driving styles and control reckless driving by taking the longitudinal/lateral acceleration and speed as input parameters.Thus,the proposed system uses fuzzy logic rather than taking the crisp values of the sensors.Results indicate that the proposed system can classify reckless driving based on fuzzy logic and,therefore,reduce the number of accidents.
文摘A gear position decision method used in automated mechanical transmission is introduced. The algorithm of the mechod is composed of a driving environment and driver's intention estimator, the shift schedules suit for each typical driving environment and driver's intention situation, and an inference ligic to determine the most proper gear position for the present situation. The estimator identifies the driving environment and driver's intention features which are divided into some typical models. Based on the identified results, the algorithm works out the best gear position. It just simulates the course of driver's making gear position decision when driving a automobile with manual transmission. The test results show that the automated mechanical transmission with the method gives less unnecessary shifting and more proper gear position than common shift schedules.
文摘在室内定位系统中,基于Wi-Fi技术的定位精度很大程度上依赖于信号的稳定,信号的多径效应与非视距(Non Line of Sight,NLOS)会增大定位误差。行人航位推算(Pedestrian Dead Reckoning,PDR)定位系统会因传感器自身误差与噪声产生累计误差。针对上述问题,提出了一种改进的PDR与最小一乘法(Least Absolute Deviation,LAD)融合的室内定位算法。该算法基于模糊逻辑将PDR算法的步长固定参数改进为变量参数,同时根据LAD的定位结果对PDR进行周期性位置与拐点位置校正,选择扩展卡尔曼滤波(Extend Kalman Filter,EKF)将改进的PDR与LAD进行融合,以降低PDR的累计误差与LAD的突变误差,提高定位精度。实验结果表明:所提方法较其他方法具有更高的定位精度。
