Modeling of individual vehicle safety and fuel consumption under comprehensive external conditions

This research intends to explore external factors affecting driving safety and fuel consumption, and build a risk and fuel consumption prediction model for individual drivers based on natural driving data. Based on 120 taxi drivers’ natural driving data during 4 months, driving behavior data under various conditions of the roadway, traffic, weather, and time of day are extracted. The driver's fuel consumption is directly collected by the on-board diagnostics (OBD) unit, and safety index is calculated based on Data Threshold Violations (DTV) and Phase Plane Analysis with Limits (PPAL) considering speed, longitudinal and lateral acceleration. By using a linear mixed model explaining the fixed effect of the external conditions and the random effect of the driver, the influences of various external factors on fuel consumption and safety are analyzed and discussed. The prediction model lays a foundation for drivers' fuel consumption and risk prediction in different external conditions, which could help improve individual driving behavior for the benefit of both fuel consumption and safety.     

Connectivity’s impact on mandatory lane-changing behaviour: Evidences from a driving simulator study

The connected environment provides driving aids to help drivers making efficient and safe driving decisions. The literature to date is devoid of conclusive evidences of the connected environment’s impact on drivers’ mandatory lane-changing (MLC) behaviour. As such, the objective of this study is to examine MLC behaviour through a driving simulator experiment using the CARRS-Q Advanced Driving Simulator. Participants with diverse background performed the experiment in randomised driving conditions: baseline (without the driving aids), connected environment with perfect communication, and connected environment with communication delay. Repeated measure ANOVA in the form of linear mixed model and Generalized Estimation Equation (GEE) are employed to analyse various driving performance indicators during MLC event. We find that drivers in the connected environment tend to wait longer, increase the initial speed, and maintain a larger spacing, compared to when they are driving in the baseline condition. In addition, drivers in the connected environment are likely to reject fewer number of gaps and select relatively bigger gap sizes. Furthermore, post-encroachment time (PET) in the connected environment is higher across different gap sizes, indicating that the connected environment makes MLC safer. The GEE model on gap acceptance suggests that the perfect communication and communication delay has positive and negative impact on the accepted gap size, respectively, and the GEE model on lane-change duration indicates that lane-change duration tends to increase in the connected environment.     

Modal analysis of real-time,real world vehicular exhaust emissions under heterogeneous traffic conditions

This study presents the characteristics of real world, real time, on-road vehicular exhaust emission namely, carbon monoxide (CO), nitric oxide (NO), hydrocarbons (HC), and carbon dioxide (CO₂) emitted under heterogeneous traffic conditions. Field experiments were performed on major category of vehicles in developing countries, i.e. two-wheelers, auto-rickshaws, cars and buses. The on-board monitoring was carried out on different corridors with varying road geometry. Results revealed that the driving cycle was dependent on the road geometry, with two lane mixed flow corridor having lot of short term events compared to that of arterial road. Vehicular emissions during idling and cruising were generally low compared to emissions during acceleration. It was also found that emissions were significantly dependent on short term events such as rapid acceleration and braking during a trip. Also, the standard emission models like COPERT and CMEM under predicted the real world emissions by 30–200% depending upon different driving modes. The on-road emissions measurements were able to capture the emission characteristics during the micro events of real world driving scenarios which were not represented by standard vehicle emission measured at laboratory conditions.     

实现汽车夜间行驶会车远光自动变近光系统的探讨

本文探讨了汽车夜间行驶中对向会车时,大灯远光产生的灯光对驾驶员视线的影响,并且基于该问题研究了大灯远近光自动切换的方案。     

架悬机车驱动装置悬挂参数及结构的研究

采用多体动力学软件SIMPACK建立完整的动力学模型,分析架悬式机车驱动装置悬挂的纵向、横向刚度对其振动加速度和机车平稳性能的影响,发现在10 kN.m-1数量级选择纵、横向刚度,可以显著改善机车的横向性能和电动机工作条件,有效降低横向轮轨力,还可以提高机车的稳定性。认为采用摆杆悬挂是实现弹性架悬的重要结构方式。提出了等效刚度的概念,分析表明在相同的等效刚度下,传统架悬模型和弹性架悬模型的计算结果是吻合的。     

驾驶疲劳机理及馈选模式研究

驾驶疲劳表现特征是多维度、多侧面。本文重点研究驾驶疲劳的致因因素及其交互作用;驾驶疲劳的形成过程、规律及机理;驾驶疲劳失衡性测试与评价,驾驶疲劳馈选模式,驾驶疲劳效应分析;驾驶疲劳综合评价体系。主要研究内容如下:1 多角度、深层次地研究驾驶行为,进而分析导致驾驶疲劳的各种直接、间接因素,建立了诸因素单独作用及相互作用的数学模型。2 研究驾驶疲劳形成过程,采用传递函数将其量化、简约化、模式化,依据自控理论将驾驶疲劳模型化为惯性环节及比例微分环节的合成。所建模型表明驾驶疲劳具有二阶系统的一般特性。3 对多种疲劳…     

基于Hyperworks的微型车车桥疲劳寿命分析与研究

驱动桥是汽车上主要部件之一,驱动桥的质量好坏会大大影响到车辆的安全使用。在车辆不断向高速、轻量、低能耗和高性能发展的今天,微型车车桥的安全性日益受到关注。本文以实体单元为基础,通过Proe软件对微型汽车车桥进行建模,利用Hyperworks对模型进行有限元模拟分析。在随机载荷下的对其疲劳寿命予以分析,并对驱动桥的疲劳强度进行了计算,得出了驱动桥的应力和变形分布。通过强度评价和疲劳寿命估算,验证了该车桥设计的合理性,为商用车车桥设计研发提供了参考。     

无水漂卵砾石地层土压平衡盾构施工关键技术

文章以北京地铁九号线丰台东大街站—丰台北路站区间全断面无水漂卵砾石地层盾构掘进工程为背景,基于地质勘查资料和试验探井的分析结果,掌握了该漂卵砾石地层的空间分布规律和力学性质。在合理选用辐条式土压平衡盾构的基础上,通过对盾构机关键部件进行适应性改造,降低了刀盘刀具磨损。施工中通过掘进参数的有效选择,实现了该类型盾构机在全断面卵砾漂石地层中的长距离掘进。     

Intelligent speed adaptation: Preliminary results of on-road study in Penang,Malaysia

The first field experiment with intelligent speed adaptation (ISA) in Malaysia was held in December 2010 in the State of Penang. Eleven private cars were instrumented with an advisory system. The system used in the present study included a vocal warning message and a visual text message that is activated when the driver attempts to exceed the speed limit. When the driver decreases the speed, the warning stops; otherwise it is continuously repeated. The test drivers drove the vehicles for three months with the installed system, and the speed was continuously logged in all vehicles. The warning was however only activated in the second month of the three month period. The present study aimed to evaluate the effects of an advisory ISA on driving speed, traffic safety, and drivers' attitude, behavior, and acceptance of the system. To examine these effects, both the survey and the logged speed data were analyzed and explored. The results show a significant reduction in the mean, maximum and 85th percentile speed due to the use of the system. However, there was no long-lasting effect on the speed when the system was deactivated. In the post-trial survey, drivers declared that the system helped them well in following the speed limits and that it assisted them in driving more comfortably. Furthermore, the warning method was more accepted compared to a supportive system, such as active accelerator pedal (AAP). After the trial, most drivers were willing to keep an ISA system.