Alcohol and traffic accidents in Japan

Alcohol induces many traffic accidents. Recently, the number of incidences of driving under the influence of alcohol (DUIA) has decreased in Japan; however, the number of drink-driving situations remains constant. Toughening the penalty has not been a particularly effective method for reducing the number of DUIAs. To reduce accidents resulting from DUIA, an approach other than sanctions may be necessary. Alcohol impairs attentional control for simple tasks and increases the propensity for risk-taking behavior; these are the major perils of DUIA. There are many risk factors for DUIA, including area, time, personal traits, the use of other substances, and alcohol use disorder. Each individual has a different risk factor for drink-driving, so it is important to provide more education for individuals and interventions for alcohol usage according to each risk factor.     

新能源微耕机驱动轮设计

为了填补大中型农机难以进入山区田地的缺陷,微型农机的设计研究是非常必要的,微型农机具有节约能源资源,促进农业增产,降低农民的劳动强度提高农业生产的效率的特点,目前自行走微耕机多采用履带式行走机构,该行走机构绝大多数采用整体式橡胶履带,由驱动轮带动橡胶履带板,从而实现整机得移动,前进后退及差速转弯。     

Study on driver model for hybrid truck based on driving simulator experimental results

In this paper, a proposed car-following driver model taking into account some features of both the compensatory and anticipatory model representing the human pedal operation has been verified by driving simulator experiments with several real drivers. The comparison between computer simulations performed by determined model parameters with the experimental results confirm the correctness of this mathematical driver model and identified model parameters. Then the driver model is joined to a hybrid vehicle dynamics model and the moderate car following maneuver simulations with various driver parameters are conducted to investigate influences of driver parameters on vehicle dynamics response and fuel economy. Finally, major driver parameters involved in the longitudinal control of drivers are clarified.     

分布式地铁列车驾驶仿真器的体系结构

从列车驾驶仿真器的体系结构出发,提出一种分布式列车驾驶仿真器的实现方案。该方案中,地铁列车驾驶仿真器由数据采集及输出、主控、ATC仿真、视景仿真、声音仿真、数字广播及通信仿真、运动平台控制等仿真子系统组成。子系统间采用IP组播的方式进行通信。方案支持基于DIS协议的仿真器间的通信,可实现多仿真器协同训练。为降低通信强度,提高仿真的实时性,采用一种DR算法。     

串联式混合动力公交车的参数选择

根据城市公交车的工作特点,对串联式混合动力公交车动力系统主要部件发动机、电动机、电池组等,提出了选择和设计原则,实现优化匹配,提高动力性、燃油经济性以及降低排放性能.     

基于主轴转速的并联混合动力环卫车驱动方案设计

传统环卫车在城市环境下燃油经济性不佳且噪声大,混合动力环卫车则有着较好的燃油经济性和较低的噪声。本文从并联混合动力环卫车驱动系统结构与工作特性出发,设计提出并联混合动力环卫车的驱动方案,分析了动力单元工作特性,确定了发动机与电机参数,并通过样车试验反映了驱动方案的合理性。     

Vehicle sensor data-based analysis on the driving style differences between operating indoor simulator and on-road instrumented vehicle

Abstract

Indoor simulator and on-road instrumented vehicle are the most popular ways to analyze driving behaviors by using collected Vehicle Sensor Data (VSD). However, for a same driver, the driving performance could be different in the real world and in the simulated world. Even though many studies have been conducted to discover the differences of driving behaviors in these two circumstances, little research has focused on analyzing the differences in driving style, which can provide more integrated knowledge of a driver from the natural structure, stimulus–response mechanism, of driving behaviors. Therefore, in this paper, the driving styles in both the real world and the simulated world are extracted by implementing the nonnegative matrix factorization method on the collected VSD data. Through this analysis, the driving style differences can be quantitatively described and discussed in detail. It is found that the drivers tend to be more unstable and sometimes aggressive when driving the simulator and the deviation in the perception of temporal gap in two circumstances is also discovered. The research findings are particularly valuable to calibrate the driving simulator and construct more reliable driving behavior models.     

基于深度学习的驾驶员状态识别

提出了基于驾驶员脸部及周围信息的驾驶员状态检测方法。文章通过实车摄像头采集了驾驶员驾驶状态视频数据,利用Dlib和OpenCV库对采集的驾驶员图像进行脸部检测,基于驾驶员脸部数据建立了深度学习数据集,然后基于该数据集设计了一种卷积神经网络模型FaceNet,利用PyTorch深度学习框架在数据集上对模型进行训练,最终得到了有较高准确率的驾驶员状态检测模型,其可识别抽烟、睡觉、左手打电话和右手打电话四种驾驶员状态。     

A measurement to driving situation awareness in signalized intersections

Drivers get involved easily in Left-Turn Across Path with Opposite Direction Traffic (LTAP-OD) conflicts at signalized intersection with unprotected left-turn phasing. This study classified the left-turn drivers’ performance into four types: (1) the correct acceptance of safe lags/gaps, (2) the correct rejection of dangerous lags/gaps, (3) the false rejection of safe lags/gaps and (4) the false acceptance of dangerous lags/gaps. Based on the high mounted video camera data, the logistic regression model was used to obtain the critical gaps for estimating whether the lags/gaps were safe or not. The results show that the cognition behavior of the left-turn drivers should be improved while the gap/lag is drawn near the critical gap. Furthermore, the conservative drivers are more likely to reject the larger lags/gaps and yield the right-of-way to the opposing vehicles. Simultaneously the poor response execution may cause drivers to have difficulties during the turning performance when the lags/gaps are large enough. These results could be used as the basis for a discussion of the right time to support the drivers.