微型电动轿车车身骨架结构分析

利用ANSYS软件对某进口微型电动轿车车身骨架进行了强度、刚度和模态计算：分析了电动轿车车身骨架的结构特点和电瓶盒等钣金件的影响，提出了独立悬架边界条件的模拟方法。     

凌志LS430轿车电动倾斜和伸缩转向柱的故障诊断与维修

介绍了2001款凌志LS430型轿车电动倾斜与伸缩转向柱的组成和功能。该转向柱系统具有故障自诊断功能。介绍了故障码B2602钥匙开锁警告开关电路故障、故障码B2610倾斜位置传感器或电动机电路故障等的诊断。另外，介绍了执行器电源电路和ECU电源电路、倾斜电动机和伸缩电动机电路等的俭修     

汽车车身结构碰撞性能的试验研究

为了在碰撞事故中有效地保护乘员的生命安全，汽车首先必须具备安全车身，以确保事故中乘员的生存空间及缓和冲击，其后应配置合理的乘员约束条件，以避免或减缓乘员与车内结构二次碰撞造成的灾害。详细介绍了车身构件碰撞性能的试验方法和研究结果。     

车身内部布置CAD系统及其应用

介绍了自行开发的汽车车身内部布置的ＣＡＤ系统，并就系统在设计中的应用了说明。该系统是在通用图形软件基础上进行二次开发而成的，以ＳＡＥ标准，ＥＥＣ标准及为主要依据，将ＣＡＤ技术运用到车身内部布置设计中。     

空调客车围护结构隔热设计方案比较研究

利用数值分析的方法，分析了空调客车非稳态情况下四种结构车厢体的二维温度场分布，计算了车厢体的二维传热损失和热桥附加传热损失。在骨架和内衬之间加一层隔热组织改善了车的隔热特性，为客车围护结构隔热优化提供了依据。     

汽车防追尾碰撞数学模型研究

为了提高车辆在高速行驶状态下的主动安全性能,研究了处于追尾行驶状态的本车与前车的运动学特征;针对前车的不同运动状态分别推导出了跟车距离的计算模型并分析了模型中3个关键参数的随机性和动态性,对制动迟滞时间提出了基于模糊推理的确定方法,对本车制动减速度和前车的运动加速度提出了比较实用的动态测算公式;另外,研究了防追尾碰撞的控制与执行,建立了动态调整安全制动停车距离的神经网络模型,提出了基于危险裕度判别的安全控制方法。     

车辆折算系数的分类及算法

提出了标准车的选择原则:车长等于约定的长度;当交通流密度很小时,以道路设计速度行驶;驾驶员遵守交通规则。定义了微观车辆折算系数:交通流全部由标准车组成时的流量与交通流全部由某种车组成时的流量之比。定义了聚类车辆折算系数:组成该类车型的所有自然车的微观折算系数与组成比例的乘积之和。定义了宏观车辆折算系数:组成混合交通流的所有自然车的微观折算系数与组成比例的乘积之和。计算了这三种车辆折算系数。     

全承载式车身结构浅析

简要说明了客车全承载车身技术的重要性和全承载车身的优越性；阐述了全承载车身的发展历程和在我国的应用现状，最后详细说明了全承载车身结构的特点及优点，使我们对于全承载车身结构技术有了一个更全面的认识。     

Autonomous cars: The tension between occupant experience and intersection capacity

Systems that enable high levels of vehicle-automation are now beginning to enter the commercial marketplace. Road vehicles capable of operating independently of real-time human control under an increasing set of circumstances will likely become more widely available in the near future. Such vehicles are expected to bring a variety of benefits. Two such anticipated advantages (relative to human-driver vehicle control) are said to be increased road network capacity and the freeing up of the driver-occupant’s time to engage in their choice of leisurely or economically-productive (non-driving) tasks.In this study we investigate the implications for intersection capacity and level-of-service of providing occupants of automated (without real-time human control), autonomously-operating (without vehicle-to-X communication) cars with ride quality that is equivalent (in terms of maximum rates of longitudinal and lateral acceleration) to two types of rail systems: [urban] light rail transit and [inter-urban] high-speed rail. The literature suggests that car passengers start experiencing discomfort at lower rates of acceleration than car drivers; it is therefore plausible that occupants of an autonomously-operating vehicle may wish to instruct their vehicle to maneuver in a way that provides them greater ride comfort than if the vehicle-control algorithm simply mimicked human-driving-operation.On the basis of traffic microsimulation analysis, we found that restricting the dynamics of autonomous cars to the acceleration/deceleration characteristics of both rail systems leads to reductions in a signalized intersection’s vehicle-processing capacity and increases in delay. The impacts were found to be larger when constraining the autonomous cars’ dynamics to the more-restrictive acceleration/deceleration profile of high-speed rail. The scenarios we analyzed must be viewed as boundary conditions, because autonomous cars’ dynamics were by definition never allowed to exceed the acceleration/deceleration constraints of the rail systems. Appropriate evidence regarding motorists’ preferences does not exist at present; establishing these preferences is an important item for the future research agenda.This paper concludes with a brief discussion of research needs to advance this line of inquiry.     

Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach

Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.