2016年长城哈弗H7胎压传感器的学习

<正>车型信息一辆2016年长城哈弗H7，车辆VIN码：LGWEF7A63GH******。功能作用车辆四轮轮胎对调或者更换胎压传感器之后，胎压报警灯点亮，就需使用该功能，对胎压进行重新学习，还需要配合胎压激活设备对传感器进行激活。     

元征X-431PAD V实测:捷豹XFL前照灯水平高度传感器校准

正元征X-431 PAD V是一款配备全新Smart Box3.0车辆诊断盒,支持乘商一体车辆诊断、ECU刷写、J2534/Do IP/CAN FD协议,具有在线编程、智能诊断、远程诊断、多项特殊功能等诸多功能的高端汽诊设备。功能说明一些车型更换新的前照灯或前照灯控制模块后,需使用前照灯水平高度传感器功能,对前照灯高度传感器进行重新校准。实测车型2018年捷豹XFL, VIN码为L2CBB3BX1JG30****(如图1所示)。     

基于地磁的车型识别模糊数据融合方法研究

把由各向异性磁阻传感器检测获得的地磁曲线视为车辆磁偶极模型的映像,通过分析车辆地磁曲线的峰值、峰谷比、峰值时间、谷值时间、车身长等特征的选择和提取,建立相应车型特征向量;然后,依据各类车型特征,应用三角形隶属度函数特征描述和模糊数据融合,提出了一种基于地磁传感器实时数据采集的车辆检测与车型识别流程和车型识别分类算法;最后,通过一组实际采集的车辆检测数据的分类结果,比较了4种模糊数据融合算式的差异,证明了车辆识别分类算法的有效性,并讨论了有待进一步研究的问题。     

2017款马自达昂克赛拉车前照灯自动调平传感器校准方法

正元征X-431 PAD V是一款具有"智能诊断"和"在线编程"功能的高端汽车故障诊断工具,具有特殊功能多、车型覆盖广、测试数据准、诊断功能强等特点。在线编程无需原厂账号,不产生费用,对车辆ECU无损害。功能说明拆装、更换前照灯自动调平传感器或更换前照灯控制模块后,需对前照灯自动调平传感器进行校准。实测车型2017款马自达昂克赛拉车。操作指引(1)连接元征X-431 PAD V汽车诊断设备,选择"马自达"车型后选择"自动搜索",系统通过VIN码自动识别车辆信息(图1)。(2)选择"快速测试"(图2),扫描出全车系统。     

基于压力传感器阵列的车型分类系统

针对当前车型分类系统识别率不高的问题,提出了用4条共聚物压电轴传感器组成的传感器阵列获取车型特征参数的方法。该方法利用传感器的特殊空间分布与车辆各车轴到达各传感器时刻的时空相关性,对采集到的信息进行数据融合,获得了多达11种车型特征参数。分类识别中用模糊统计的方法对大量的样本车型特征参数处理后,构造了隶属函数,最后采用了D-S证据理论对车型进行分类。实践证明该方法具有较高的识别率。     

便携式道路载荷谱测试系统硬件设计

车辆超限超载现象严重危害道路基础设施,为保证公路交通的有效运营、管理和维护,设计了能够采集并存储车辆数量、车速和车辆载重的道路载荷谱测试系统。将压电膜传感器安装于道路中,车辆经过传感器时产生电荷信号,并经过电荷放大电路转换电压信号;单片机具有AD采集功能,可以快速采集并处理输入电压信号,计算得到车辆参数信息。系统能接收地感线圈开关信号,并将车辆状态传递至单片机;设计了RS-232串口通讯电路外传测试数据并接收控制参数;设计了实时时钟电路,用于获取车辆经过时间,并随车辆数据一起存储至SD卡中。经过室外场地安装测试结果表明,测量的轴重等数据具有较好的精度,能够满足高速公路车辆动态测试的要求。     

三菱车系部分车型相关系统设定方法(一)

<正>在维修三菱车的过程中,往往会因为清洗系统、更换零件、增加或改变功能、排除故障等,需要对车辆某些系统和传感器进行归零、匹配、学习、设定等操作,笔者根据数年维修经验,汇集了三菱车系的具体操作方法,方便大家维修时使用。1指南针的设定方法1.1三菱V3系列车型用指南针设定方法三菱V3系列车型指南针采用磁通量闸门式方位传感器,它是利用地磁进行检测的传感器,指南针指向显示有两     

使用朗仁H6远程进行名爵GS锐腾变速器匹配

车辆信息:2016年名爵GS锐腾.案例背景:车辆更换变速器电脑后需要做匹配学习,朗仁技术中心工程师利用远程协助帮助维修人员完成匹配学习. 该车型有以下常用功能:ABS的压力传感器学习、间接式胎压系统重置、偏航传感器学习、ABS加液排气;电动转向系统的电动助力转向系统学习;AFS动态前照灯水平调节系统的DHL高度传感器零...     

使用元征诊断设备执行胎压传感器学习——以2014款海马S7车为例

<正>元征X-431 PAD V是一款具有“智能诊断”和“在线编程”功能的高端汽车故障诊断工具,具有特殊功能多、车型覆盖广、测试数据准、诊断功能强等特点。在线编程无需原厂账号,不产生费用,对车辆ECU无损害。功能作用更换胎压传感器后,需要对胎压传感器执行学习。实测产品元征X-431 PAD V和TSGUN胎压检测手持终端。实测车型2 0 1 4款海马S 7车,整车型号为HMC7200B4T1。     

雪佛兰车系故障三例

<正>案例1车型:2010款科鲁兹。行驶里程:58591km。VIN:LSGPC54U0AF××××××。故障现象:发动机故障灯亮。故障诊断:连接GDS2+MDI检查,发动机控制模块设置了1个故障码:P013100加热型氧传感器电路电压过低-传感器1;查看发动机数据显示氧传感器1的信号电压为0。拔下氧传感器连接器,用试灯连接氧传感器加热器控制搭铁端1脚和加热器电源端3脚,用GDS2的特殊功能驱动加热器结果试灯闪亮,说明加热器电路     

基于视觉的前方车辆探测技术研究方法综述

对国内外关于车道上本车前方车辆检测方法进行了综述。根据探测车辆所采用的传感器不同,分别对采用单目视觉、双目视觉、视觉与其他传感器融合以及非视觉传感器的几种探测方法进行了比较与分析。同时,针对每种探测方法,进一步阐述了目前车辆识别的一些具体算法。最后,指出了每种方法存在的优点和一些不足。     

A New Method for Accurately Estimating the Weight of Moving Vehicles Using Piezoelectric Sensors and Adaptive-footprint Tire Model

Summary This paper presents new methods for estimating the axle weight of a moving vehicle, using two piezoelectric sensors and adaptive-footprint tire model. It is more difficult to weigh vehicles in motion accurately than to weigh standing vehicles. The difficulties in weighing moving vehicles result from sensor limitations as well as dynamic loading effects induced by vehicle/pavement interactions. For example, two identical vehicles with the same weight will generate sensor signals that differ in the shape and the peak value, depending the tire pressure, vehicle speed, road roughness, and sensor characteristics. This paper develops a method that is much less sensitive to these variable factors in determining the axle weight of a moving vehicle. In the developed method, first the piezoelectric sensor signal is reconstructed using the inverse dynamics of a high-pass filter representing the piezoelectric sensor. Then, the reconstructed signal, is normalized, using the nominal road/tire contact length obtained using an adaptive-footprint tire model, and then integrated. Experiments are performed with 3 vehicles of known weight ranging from 1,400 kg to 28,040 kg. The developed method is compared to two other algorithms. Results show that the developed method is most consistent and accurate.     

面向低速清扫车的信息融合车辆跟踪方法

交通参与者运动的准确跟踪与预测对智能车行为决策的有效性至关重要。传统运动目标的跟踪系统多采用单一传感器,难以保证数据的精度与可信度。为提高系统的鲁棒性与可靠性,设计一种融合毫米波雷达和相机的目标跟踪方案,该方案针对多目标的特征级信息进行融合。首先,考虑低速行驶的自动驾驶清扫车所处环境杂波较多,方案选择基于IMM/JPDA的多目标跟踪方法估计局部航迹。为降低JPDA数据关联的计算复杂度,结合基于马氏距离构造的椭圆关联门和基于车辆非完整性约束构造的扇形关联门,实现关联门的自适应调整,减少关联杂波的干扰。其次,结合传感器的配置与特性,对目标的航迹状态进行空间对准和时间对准,按照航迹点间的欧氏距离和互协方差选择融合模式,进行局部航迹融合。最后,为验证多目标跟踪和航迹融合方法的有效性与实用性,分别设计基于MATLAB/PreScan环境的仿真试验和基于智能清扫车平台的实车试验。研究结果表明：在横、纵方向上,融合后的系统状态都比单一传感器的估计状态更为准确,融合结果对单一传感器的估计误差有35%以上的提升;实车试验证明,该方案能有效融合ESR毫米波雷达和Mobileye单目前视相机的状态估计信息,能基本正确地跟踪目标和估计航迹;融合状态的横、纵向误差都在可接受范围以内,且融合状态比单一传感器的估计波动更小。     

Low cost design of parallel parking assist system based on an ultrasonic sensor

This paper presents a low cost design and implementation of a parallel parking assist system (PPAS) based on ultrasonic sensors. Generally, a PPAS requires several types of sensors, such as an ultrasonic sensor, camera sensor, radar sensor and laser sensor for parking space detection. However, our proposed PPAS only requires two ultrasonic sensors on the front and lateral sides for parking space detection. Moreover, a steering angle sensor and wheel speed sensor installed in the vehicle are used to obtain vehicle position information for localization in ultrasonic range data. The hardware architecture of the PPAS based on an electronic control unit (ECU) module, sensor modules and a human machine interface (HMI) module was proposed. Moreover, the software architecture of the PPAS is based on system initialization, scheduling, recognition and a control algorithm. In particular, a novel sensor algorithm was proposed to minimize the vehicle corner error of the ultrasonic sensor. A prototype of the PPAS based on the proposed architecture was constructed. The experimental results demonstrate that the implemented prototype is robust and successfully performs parking space detection and automatic steering control. Finally, the low cost design and implementation of the PPAS was possible due to the cheap ultrasonic sensors, simple hardware design and low computational complexity of the proposed algorithm.     

汽车轮速传感器仿真方法

轮速传感器测量汽车轮速信号,用于制动、发动机及变速箱等众多系统控制,是汽车最关键的部件之一。新车型开发阶段,为了对汽车制动防抱死系统(ABS)及早有效的开发验证,需要对轮速传感器进行仿真模拟。文章针对最常用的主动式轮速传感器进行测试与分析,通过设计信号调理电路,成功搭建了ABS硬件在环仿真平台,既简化了汽车开发阶段的验证与测试,又节省了开发成本。     

车速传感器试验台的系统开发

设计了ABS车速传感器试验台,分析设计结构及其车速传感器所采集的信号调理电路并与微机进行实时的通信,实时监测车速传感器的性能参数,开发了数据采集系统进行数据分析处理。研究表明该系统性能稳定,抗干扰能力强,为车速传感器测试提供了方便。     

A New Method for Accurately Estimating the Weight of Moving Vehicles Using Piezoelectric Sensors and Adaptive-footprint Tire Model

Summary This paper presents new methods for estimating the axle weight of a moving vehicle, using two piezoelectric sensors and adaptive-footprint tire model. It is more difficult to weigh vehicles in motion accurately than to weigh standing vehicles. The difficulties in weighing moving vehicles result from sensor limitations as well as dynamic loading effects induced by vehicle/pavement interactions. For example, two identical vehicles with the same weight will generate sensor signals that differ in the shape and the peak value, depending the tire pressure, vehicle speed, road roughness, and sensor characteristics. This paper develops a method that is much less sensitive to these variable factors in determining the axle weight of a moving vehicle. In the developed method, first the piezoelectric sensor signal is reconstructed using the inverse dynamics of a high-pass filter representing the piezoelectric sensor. Then, the reconstructed signal, is normalized, using the nominal road/tire contact length obtained using an adaptive-footprint tire model, and then integrated. Experiments are performed with 3 vehicles of known weight ranging from 1,400 kg to 28,040 kg. The developed method is compared to two other algorithms. Results show that the developed method is most consistent and accurate.     

Evaluation of detection sensitivity and count performance of advanced vehicle detection technologies at a signalized intersection

This article evaluates the stop-bar detection and count performance of three advanced vehicle detection sensors under various environmental conditions at a signalized intersection. Continuing advancements to vehicle detection technologies and improvements to their detection capabilities to overcome issues from impacting conditions necessitates testing the performance of new and upgraded sensor products to evaluate their performance and identify the most suitable products for various climates and weather conditions. The three evaluated sensors were Autoscope Encore video, Iteris Vantage Edge 2 video, and Wavetronix SmartSensor Matrix microwave sensors. The three sensors performed with high detection sensitivity during ideal environmental conditions with up to 99.9% detection accuracy levels and are suitable for traffic monitoring centers that rely on remote access to the monitored sites and the collected data. However, they were affected by some extreme adverse weather conditions, mainly daytime and nighttime snow, daytime fog, dawn lighting, and strong winds (for high mounted devices). The selection of a sensor product will depend on the type of application and the priority given to the type of traffic data being collected. Overall, the Iteris video sensor performed with the highest detection sensitivity levels, with the Wavetronix Matrix microwave sensor performing similarly under most conditions (14 of 19 evaluated conditions). Autoscope video provided the highest count accuracies and also provides a much broader data collection capability. The results of this study will help transportation agencies in selecting suitable vehicle detection sensor technologies for future installations within their jurisdiction and improved data collection.