Calibration of nonlinear car-following laws for traffic oscillation prediction

Frequency-domain analysis has been successfully used to (i) predict the amplification of traffic oscillations along a platoon of vehicles with nonlinear car-following laws and (ii) measure traffic oscillation properties (e.g., periodicity, magnitude) from field data. This paper proposes a new method to calibrate nonlinear car-following laws based on real-world vehicle trajectories, such that oscillation prediction (based on the calibrated car-following laws) and measurement from the same data can be compared and validated. This calibration method, for the first time, takes into account not only the driver’s car-following behavior but also the vehicle trajectory’s time-domain (e.g., location, speed) and frequency-domain properties (e.g., peak oscillation amplitude). We use Newell’s car-following model (1961) as an example and calibrate its parameters based on a penalty-based maximum likelihood estimation procedure. A series of experiments using Next Generation Simulation (NGSIM) data are conducted to illustrate the applicability and performance of the proposed approach. Results show that the calibrated car-following models are able to simultaneously reproduce observed driver behavior, time-domain trajectories, and oscillation propagation along the platoon with reasonable accuracy.     

Multilane first-order traffic flow model with endogenous representation of lane-flow equilibrium

In this study, we develop a multilane first-order traffic flow model for freeway networks. In the model, lane changing is considered as a stochastic behavior that can decrease an individual driver’s disutility or cost, and is represented as dynamics toward the equilibrium of lane-flow distribution along with longitudinal traffic dynamics. The proposed method can be differentiated from those in previous studies because in this study, the motivation of lane changing is explicitly considered and it is treated as a utility defined by the current macroscopic traffic state. In addition, the entire process of lane changing is computed macroscopically by an extension of the kinematic wave theory employing IT principle; moreover, in the model framework, the lane-flow equilibrium curve is endogenously generated because of self-motivated lane changes. Furthermore, the parsimonious representation enables parameter calibration using the data collected from conventional loop detectors. The calibration of the data collected at four different sites, including a sag bottleneck, on the Chugoku expressway in Japan reveals that the proposed method can represent the lane-flow distribution of any observation site with high accuracy, and that the estimated parameters can reasonably explain the multilane traffic dynamics and the bottleneck phenomena uphill of sag sections.     

整车远程标定技术的应用研究

整车标定是优化控制器内软件参数、协调不同控制器协同工作,以实现车辆各项性能来满足用户和国家法规要求的过程,在整车产品开发过程中占据着核心位置。整车标定的工作任务量很大,包括覆盖不同的温度和海拔环境,往往需要较长的开发周期。随着控制器的不断复杂化,以及排放、油耗等各项国家法规的不断升级,在有限的开发周期内,整车标定面临的压力越来越大。因此,传统的标定方式、方法已经不适应较短的开发周期和投放市场时间要求,远程标定是提升整车标定高效率、高精度的重要技术手段之一。通过分析远程标定实现的方法、原理,结合整车标定的工作属性,对远程标定在整车开发过程中的应用以及发展前景进行分析和探讨。     

浅析台架测试精度校准方法与影响

台架测试是发动机开发最重要的一个环节,测试数据能够充分地反馈发动机的整体性能.燃油经济性、动力性,以及发动机NVH测试反馈的舒适性都能够在台架测试中得到准确的数据反馈.设备的精度高低是得到有效数据的重点要求,文章对量检具的核查的方法以及要求进行充分介绍.     

基于多源信息融合的行人检测实时性方法研究

目前车辆安全预警系统中存在行人检测粗检不准,细检过慢的问题,孤立的采用激光、雷达、红外或是摄像机均很难满足行人检测实时而准确的需求.考虑用多源信息融合的方法进行行人检测即将雷达与摄像机结合运用,使它们的信息有机融合,在提高速度的同时又不以降低精度为代价.研究了适应车载安全系统的摄像机标定与确定行人重点检测区域的方法.     

高压共轨EGR中型柴油机OBD标定技术探讨

按照法规要求,在满足国Ⅳ排放法规的同时,必须加装OBD系统。本文主要介绍了匹配了BOSCH电控高压共轨系统和EGR＋DOC＋POC方案的中小型发动机,OBD的基本原理和标定方法流程。     

联网高速公路有效路径伸展系数的标定

采用启发式搜索算法标定联网高速公路路径伸展系数的值,确定高速公路路网有效多路径搜索范围,利用改进的标号法算法搜索给定路网上任两站点之间的有效多路径。提出联网高速公路多路径交通量计算模型和各路段流量偏差计算模型,分别按最短路法和实际行驶路径（调查取得）在有效多路径集合上进行流量加载,得到有效多路径上交通流漂移量。利用上述模型,对山东省高速公路车辆行驶路径调查数据进行统计分析,标定得出山东省高速公路网路径伸展系数为0．3,出行有效多路径交通量占所有路径总交通量的比例为6.1‰。     

BP神经网络在女性假人颈部标定试验中的应用

采用BP神经网络代替响应曲面法的多项式函数,建立了基于BP神经网络的女性假人颈部减速特性模型.该模型以摆锤冲击速度、标定温度、蜂窝铝孔数、蜂窝铝切割形式为输入,10ms时刻颈部减速度为输出,实现了颈部减速特性的拟合预测功能.两种方法的预测结果表明,基于BP神经网络法的预测误差最大值为0.04m/s,而响应曲面法的预测误差最大值为0.05 m/s.     

479Q汽油机电控参数标定研究

利用AVL PUMA OPEN汽车动力测试台架对479Q发动机进行稳态电控参数的标定。为了寻求ECU与发动机的最佳配合,利用神经网络建立电控参数预测模型,通过对神经网络模型进行训练预测出稳态工况下发动机连续的ECU控制参数,并进行检验。研究结果表明,该预测模型具有较强的泛化能力,能够准确地预测出发动机电控参数。