Weather and road geometry impact on longitudinal driving behavior: Exploratory analysis using an empirically supported acceleration modeling framework

The objective of this paper is to quantify and characterize driver behavior under different roadway geometries and weather conditions. In order to explore how a driver perceives the rapidly changing driving surrounding (i.e. different weather conditions and road geometry configurations) and executes acceleration maneuvers accordingly, this paper extends a Prospect Theory based acceleration modeling framework. A driving simulator is utilized to conduct 76 driving experiments. Foggy weather, icy and wet roadway surfaces, horizontal and vertical curves, and different lane and shoulder widths are simulated while having participants driving behind a yellow cab at speeds/headways of their choice. After studying the driving trends observed in the different driving experiments, the extended Prospect Theory based acceleration model is calibrated using the produced trajectory data. The extended Prospect Theory based model parameters are able to reflect a change in risk-perception and acceleration maneuvering when receiving different parameterized exogenous information. The results indicate that drivers invest more attention and effort to deal with the roadway challenges compared to the effort to deal with the weather conditions. Moreover, the calibrated model is used to simulate a highway segment and observe the produced fundamental diagram. The preliminary results suggest that the model is capable of capturing driver behavior under different roadway and weather conditions leading to changes in capacity and traffic disruptions.     

Development of a representative urban driving cycle construction methodology for electric vehicles: A case study in Xi’an

This paper develops a systematic and practical construction methodology of a representative urban driving cycle for electric vehicles, taking Xi’an as a case study. The methodology tackles four major tasks: test route selection, vehicle operation data collection, data processing, and driving cycle construction. A qualitative and quantitative comprehensive analysis method is proposed based on a sampling survey and an analytic hierarchy process to design test routes. A hybrid method using a chase car and on-board measurement techniques is employed to collect data. For data processing, the principal component analysis algorithm is used to reduce the dimensions of motion characteristic parameters, and the K-means and support vector machine hybrid algorithm is used to classify the driving segments. The proposed driving cycle construction method is based on the Markov and Monte Carlo simulation method. In this study, relative error, performance value, and speed-acceleration probability distribution are used as decision criteria for selecting the most representative driving cycle. Finally, characteristic parameters, driving range, and energy consumption are compared under different driving cycles.     

A novel classification method for driving simulators based on existing flight simulator classification standards

As technology has advanced and costs have fallen, the advantages of using simulators to train for safe, economical, and environmentally friendly driving have become more apparent. The need for a driving simulator classification arises from understanding and comparing simulator capabilities and options; however, only a limited number of studies have been conducted related to classification, calling for determination of methods and criteria. In this study, a classification method for driving simulators is proposed by adapting criteria for helicopter flight simulation training devices in which established methods of classification are defined by international and national regulators such as the Joint Aviation Authorities and Federal Aviation Administration. In the proposed method, the level of a simulator is determined by taking general characteristics under consideration, such as motion, visual, and sound systems. Through a case study, the method was applied to determine the class of a specific truck simulator.     

基于视频识别技术的无信号交叉口安全预警系统原型开发

基于视频识别技术开发无信号交叉口安全预警系统原型,在Visual Studio 2010开发环境下,借助halcon8.0图像处理算子,应用背景差法识别与跟踪运动车辆,实时采集车速、加速度、距离等微观交通信息,建立无信号交叉口安全通行模型实现动态安全预警功能,利用模型车辆测试系统的预警可靠性,在参数设置合理的条件下取得了87%的预警成功率。     

压缩空气动力汽车的基本原理及可行性分析

压缩空气动力汽车又称气动汽车,是一种“零排放”的新能源汽车。文章对气动汽车的优点和气动汽车的基本结构及工作原理进行介绍,然后利用数值分析的方法对气动汽车续驶里程进行分析,从而验证了其可行性。     

基于复杂网络的列车辅助驾驶危险致因传播模型

为了解预期功能安全(SOTIF)相关危险致因在基于智能感知的列车辅助驾驶系统 (IATDAS)中的传播特性，提升针对该类系统的危险控制能力，本文提出基于复杂网络的IATDAS 系统危险致因传播模型。该模型在SOTIF危险致因网络的基础上，提供了全局容量-负载传播机制，能有效刻画IATDAS系统的危险致因传播机制。案例分析结果表明：本文所提模型能够解决复杂致因关系下既有模型与系统实际情况不符的问题，如对于具有较长后续传播路径的致因，本文模型能够刻画其较难导致危险的实际特征；依据本文模型实施传播控制，可以显著降低危险致因的传播速度，如对影响节点范围大、前期影响节点数量增加快的危险因素进行控制时，可使其平均传播速度降低68%，比随机控制策略多降58%。该模型可以为IATDAS系统的SOTIF相关危险控制提供决策基础。     

基于细-宏观数据交互的桥梁水毁数值仿真

为精细化探究桥梁水毁倒塌机理，基于计算流体力学与泥沙动力学数值仿真，得到桥梁冲刷形态演变与波流力时程数据，构建桥梁水毁全过程的连续倒塌数值模型；通过开发细-宏观数据交互接口，将冲刷所致边界条件改变与波流力对桥梁结构作用效应实时传递至结构域桥梁连续倒塌数值模型，高精度连续仿真冲刷发展与波流力作用下桥梁水毁全程力学响应与结构形态演化；最后，通过河源东江大桥倒塌案例分析，验证该仿真方法与数据交互接口的正确性和有效性。结果表明：河源东江大桥连续倒塌可能由冲刷掏空基础后倾斜、拱脚薄弱节点失效以及非制动墩承受不平衡水平推力所致；所提出的仿真方法可实现冲刷仿真、波流力计算与结构倒塌分析的同步交互实施，可为从水文源头探究桥梁水毁失效机理与倒塌模式提供精准高效的分析工具，为桥梁抗水设计、评估以及未来规范修订提供理论基础与技术手段。     

Driving cycles that reproduce driving patterns,energy consumptions and tailpipe emissions

This study presents the Energy Based Micro-trip (EBMT) method, which is a new method to construct driving cycles that represent local driving patterns and reproduce the real energy consumption and tailpipe emissions from vehicles in a given region. It uses data of specific energy consumption, speed, and percentage of idling time as criteria of acceptable representativeness. To study the performance of the EBMT, we used a database of speed, fuel consumption, and tailpipe emissions (CO₂, CO, and NO_x), which was obtained monitoring at 1 Hz, the operation of 15 heavy-duty vehicles when they operated within different traffic conditions, during eight months. The speed vs. time data contained in this database defined the local driving pattern, which was described by 19 characteristic parameters (CPs). Using this database, we ran the EBMT and described the resulting driving cycle by 19 characteristics parameters (CPs*). The relative differences between CPs and CPs* quantified how close the obtained driving cycle represented the driving pattern. To observe tendencies of our results, we repeated the process 1000 times and reported the average relative difference (ARD) and the interquartile range (IQR) of those differences for each CP.. We repeated the process for the case of a traditional Micro-trip method and compared to previous results. The driving cycles constructed by the EBMT method showed the lowest values of ARDs and IQRs, meaning that it produces driving cycles with the highest representativeness of the driving patterns, and the best reproduction of energy consumption, and tailpipe emissions.     

Development of two driving cycles for utility vehicles

Driving cycles are used to assess vehicle fuel consumption and pollutant emissions. The premise in this article is that suburban road-work vehicles and airport vehicles operate under particular conditions that are not taken into account by conventional driving cycles. Thus, experimental data were acquired from two pickup trucks representing both vehicle fleets that were equipped with a data logger. Based on experimental data, the suburban road-work vehicle showed a mixed driving behavior of high and low speed with occasional long periods of idling. In the airport environment, however, the driving conditions were restricted to airport grounds but were characterized by many accelerations and few high speeds. Based on these measurements, microtrips were defined and two driving cycles proposed. Fuel consumption and pollutant emissions were then measured for both cycles and compared to the FTP-75 and HWFCT cycles, which revealed a major difference: at least a 31% increase in fuel consumption over FTP-75. This increased fuel consumption translates into higher pollutant emissions. When CO₂ equivalent emissions are taken into account, the proposed cycles show an increase of at least 31% over FTP-75 and illustrate the importance of quantifying fleet speed patterns to assess CO₂ equivalent emissions so that the fleet manager can determine potential gains in energy or increased pollutant emissions.