基于扩展卡尔曼滤波算法的智能泊车航位推算研究

针对智能泊车系统对高精度车辆航位估计的要求,设计了基于轮速及方向盘转角信号的扩展卡尔曼滤波航位推算算法.离线仿真实验将本算法与传统航位推算算法进行了对比,结果显示本算法具有更高的精度;实车试验进一步验证了本算法的实际应用价值.     

改进联合滤波在GPS/DR组合定位中的应用

根据低成本车辆GPS/DR组合定位系统传感器精度低和计算能力弱的特点,提出一种改进联合卡尔曼滤波(FKF)算法,并简化主滤波器信息融合算法,稍微降低融合精度,提高计算效率。试验结果表明,提出的改进联合滤波算法具有融合精度高、容错性好、计算量小、便于工程实现等优点。     

神经元实时辨识车辆导航系统中的GPS多径误差

在城市车辆导航过程中,多径误差是近距离差分GPS等高精度定位的主要误差源。文章首次提出神经元实时辨识GPS多径误差方法,它能实时辨识当前时刻GPS接收机输出的GPS信号是否含有多径误差,从而解决GPS多径误差对城市车辆导航定位精度的影响。把该方法用到实际工程中,其结果显示能够有效的消除GPS多径误差对城市车辆导航系统定位精度的影响。     

A Trajectory-Based Map-Matching System for the Driving Road Identification in Vehicle Navigation Systems

Driving road identification is the key issue of a vehicle navigation system that supports various services of intelligent transportation systems. The method for driving road identification is also known as map matching (MM). In spite of the development of MM algorithms, limitations still exist in obtaining the positioning data and preparing candidate roads (CRs) that may result in mismatches in some special difficult road configurations such as flyovers and parallel roads. To overcome the limitations, an integrated trajectory-based MM (tbMM) system is proposed based on the trajectory similarity evaluation method. The system can fuse the information from global positioning systems (GPS) and inertial sensors to generate the vehicle trajectory that represents the vehicle continuous movement in three dimensions. The elevation data of vehicle and roads are involved to enhance the trajectory-based matching process. Also the method employs an optimized mechanism for generating and maintaining CRs. Using the mechanism, separated road segments in a digital map are reorganized in the form of possible driving roads and the topology among them is guaranteed. Moreover, the CRs are obtained considering all the possibilities in determining the driving road so that the valuable historical information can be effectively reserved to provide more reliable matches in ambiguous situations. The tbMM system was evaluated using a number of real-world vehicle-level test datasets in urban areas in Beijing. Also a comparison test was performed to evaluate the driving road identification accuracy against existing MM algorithms. The results show that the tbMM system can provide reliable matches with about 99% accuracy in all the difficult scenarios and outperforms the existing algorithms.     

Developing travel time estimation methods using sparse GPS data

Existing methods of estimating travel time from GPS data are not able to simultaneously take account of the issues related to uncertainties associated with GPS and spatial road network data. Moreover, they typically depend upon high-frequency data sources from specialist data providers, which can be expensive and are not always readily available. The study reported here therefore sought to better estimate travel time using “readily available” vehicle trajectory data from moving sensors such as buses, taxis, and logistical vehicles equipped with GPS in “near” real time. To do this, accurate locations of vehicles on a link were first map-matched to reduce the positioning errors associated with GPS and digital road maps. Two mathematical methods were then developed to estimate link travel times from map-matched GPS fixes, vehicle speeds, and network connectivity information with a special focus on sampling frequencies, vehicle penetration rates, and time window lengths. Global positioning system (GPS) data from Interstate I-880 (California) for a total of 73 vehicles over 6 h were obtained from the University of California Berkeley's Mobile Century Project, and these were used to evaluate several travel time estimation methods, the results of which were then validated against reference travel time data collected from high resolution video cameras. The results indicate that vehicle penetration rates, data sampling frequencies, vehicle coverage on the links, and time window lengths all influence the accuracy of link travel time estimation. The performance was found to be best in the 5-min time window length and for a GPS sampling frequency of 60 s.     

基于消防安全疏散标志的高精度室内视觉定位

为解决室内交通无法利用GPS进行定位的问题,针对室内普遍存在并且均匀分布的消防安全疏散标志,研究了基于消防安全疏散标志的高精度室内视觉定位算法.以计算当前位置距离地图中最近的1个消防安全疏散标志地点的位姿为目标,利用消防安全疏散标志的颜色特性进行颜色阈值分割.结合方向梯度直方图(HOG)特征与支持向量机(SVM)检测候选框中是否含有消防安全疏散标志,然后用加速鲁棒特征(SURF)全局特征进行特征匹配,利用最邻近(KNN)方法选取全局特征距离最小的K个地点作为候选定位结果.用SURF局部特征进行特征匹配,选取局部特征匹配数目最多的1个地点作为图像级定位结果,并计算当前位置在地图中的位姿.通过在地下停车场和大型办公楼进行实地测试,图像级定位的准确率在96% 以上,平均定位误差在0.6 m以下.实验结果表明,该算法满足了室内定位精度的需求,并具有良好的鲁棒性.      

高精度车载导航定位系统的研究

通过对车载导航系统结构、功能的分析，对车辆的高精度导航定位进行了研究。在常见的卫星导航（GPS）、航位推算（DR）等导航定位的基础上，进一步研究并实现了地图匹配算法，克服了GPS信号受阻时定位间断或失效的缺点，避免了航位推算定位误差随时间的积累。通过大量的跑车试验表明，基于GPS／DR／MM的车辆导航系统，可以高精度地实现车辆定位，进而可以实现地理信息的查询、交通诱导等功能。具有重要的实际应用价值。     

Integration of GPS and monocular vision for land vehicle navigation in urban area

For land vehicle navigation in urban area, Global Positioning System (GPS) receivers often suffer from the lack of positioning accuracy, availability, and continuity due to insufficient number of visible satellites and multipath errors. To mitigate this problem, this paper proposes an efficient hybrid positioning method combining a single frequency GPS receiver and a monocular vision sensor. The proposed method is advantageous in that it requires only low-cost hardware and no external map aiding. Compared with existing vision-based methods, the proposed method directly measures absolute heading angle based on the images of straight road segments. For the reason, the proposed method is resilient to multipath errors. The performance of the proposed method is evaluated by the experiments with field-collected real measurements; one with good satellite visibility and the others with poor satellite visibility. Comparison with existing positioning methods demonstrates the feasibility of the proposed method in urban area.     

车载导航系统陀螺的自适应校正方法

分析了低成本压电振动陀螺误差及其影响因素,在实验的基础上得出采用温度补偿陀螺误差的可行性。建立了联合卡尔曼滤波方程融合GPS和INS信息,估计定位信息和陀螺误差。提出车载GPS/INS组合导航系统中陀螺零漂误差和标度因子误差的校正过程启动条件,当条件满足时,以估计的陀螺误差为输入,采用温度误差校正表学习算法对陀螺误差模型进行训练。用道路实验数据对提出的陀螺校正算法进行验证,结果表明该算法精度高、收敛快、可操作性好。     

面向智能网联汽车定位的协同地图匹配算法

为实现智能网联环境下低成本、高精度的车辆定位, 研究了基于自适应遗传Rao-Blackwellized粒子滤波的协同地图匹配算法。利用联网车辆的定位信息和道路约束条件消除公共偏差, 提高车辆定位精度。将自适应遗传算法引入到粒子滤波的重采样过程中, 增加粒子的多样性, 解决传统粒子滤波算法中容易出现的“粒子退化”和“粒子耗尽”问题。通过仿真实验与传统粒子滤波以及卡尔曼平滑粒子滤波下的定位结果进行了对比, 同时分析了不同联网车辆数目对定位精度的影响。通过实际测试验证了算法在实际应用中的定位效果。实测结果表明: 以典型十字路口为例, 在联网车辆数目为4的情况下, 协同地图匹配算法的定位误差范围为1.67 m, 分别为原始GNSS定位以及单车地图匹配定位结果的41.03%和56.80%。同时, 该算法的统计定位精度(CEP)达到1.06 m, 比GNSS原始定位精度提高了2.52 m, 具有较好的定位效果。      

基于模糊逻辑的导航定位数据校正算法

为了提高GPS/DR组合定位系统的定位精度,通常采用地图匹配算法来修正定位误差.文中采用了一种基于模糊逻辑的导航定位数据校正算法,对经联合卡尔曼滤波输出的GPS/DR的定位数据进行校正.通过Matlab仿真实验,结果表明,该算法能有效地减小误差,提高组合定位系统的定位精度,改善其对航线跟踪的质量.     

基于ARM9的汽车行驶记录仪设计

汽车行驶记录仪的使用对遏制疲劳驾驶与车辆超速以及进行道路交通事故的鉴定具有重要作用。基于国家标准（GB／T19056-2012）设计汽车行驶记录仪,在系统整体设计的基础上,硬件选用S3C2440微处理器,对数据采集等模块进行设计,其中GPS模块定位误差达到10m,速度误差达到0．1m／s,软件采用嵌入式Linux系统。该记录仪在对基本行驶状态信息记录的基础上,实现了对汽车的精准定位、远程跟踪和数据通信传输功能。     

基于RFID和IMU技术的室内定位方法研究与应用

针对在室内停车场等GPS信号较差的工况,行驶车辆无法实时获取准确定位的问题,文章结合RFID和IMU两种定位技术,使用卡尔曼滤波器耦合该两组定位系统信号,并将该方法应用在某款车型上,结果表明在室内停车场能够获得实时准确的位置信息。     

考虑动力学模型系统误差补偿的智能车GNSS/IMU组合定位算法

为了解决智能车动态组合定位过程中，因动力学模型与实际模型之间存在偏差导致滤波精度下降的问题，针对智能车全球导航卫星系统(GNSS)/惯性测量单元(IMU)组合定位系统，结合非线性预测滤波(NPF)和自适应滤波的优点，提出了一种考虑动力学模型系统误差实时估计和补偿的自适应非线性预测滤波(ANPF)算法。首先，根据NPF算法原理，通过最小化预测观测残差与系统误差的加权平方和，估计动力学模型系统误差；其次，结合自适应滤波原理，利用状态预测残差向量构造自适应因子，设计了一种自适应扩展卡尔曼滤波(AEKF)算法，用于估计系统状态向量，并通过自适应因子抑制动力学模型系统误差和线性化误差对系统状态估计精度的影响，克服NPF对系统状态估计精度有限的缺陷；再次，对动力学模型系统误差的估计误差和由动力学模型系统误差引起的系统噪声的等效协方差阵进行了分析和推导，以补偿动力学模型系统误差对系统状态估计的影响；最后，通过车载GNSS/IMU组合定位系统试验，从算法精度、鲁棒性和实时性方面对提出的算法和其他滤波算法的性能进行了验证和对比分析。研究结果表明:提出的自适应算法继承了NPF算法简易性和高实时性的优点，同时克服了NPF算法估计精度有限的缺陷，具有较好的滤波解算精度，水平定位精度小于1.0 m，算法单次平均执行时间约为0.013 9 ms，在精度和实时性的平衡方面显著优于其他滤波方法。     

Accurate INS position solution during GPS outages or degraded GPS

Temporary degraded GPS (DGPS) position loss, in circumstances such as an overhead bridge, can be alleviated by an inertial navigation system (INS) that uses onboard sensors, such as yaw and speed sensors, to determine vehicle position. This paper introduces a post-processing DGPS/INS integration approach based on using the INS solution during DGPS outages or periods of low accuracy DGPS position solutions. In this approach, the INS solution initialization is performed using the DGPS solution before DGPS position solution loss, and measurements from the Inertial Measurement Unit (IMU). The final post-processed INS solution is a weighted average of the INS forward and backward solutions. This work constitutes two parts: the INS initialization methods for different degrees of freedom vehicle positioning models, and the developed weighting model necessary to combine the forward and the backward solutions. The former part is essential in obtaining acceptable INS initial states for both the stand-alone INS or any post-processing or real time INS/GPS integrated system. The latter part is based on the use of the complementary error behaviours of the backward and the forward solutions, and can be used as a survey method with acceptable position solutions accuracies. Applying the forward/backward INS combined solution method on real data shows that the resultant INS solution accuracy is 35 cm or less over a 1000 m road segment. This method is used to survey freeways interchange road segments where 50% of the surveyed distance has no DGPS solution or has a degraded DGPS solution. The average achieved accuracy over the whole freeways interchange is around 40 cm over a 23 km distance.     

改进粒子滤波算法在组合导航中的应用

为了提高组合导航定位系统的定位精度和可靠性,分别对扩展卡尔曼滤波(EKF)、粒子滤波(PF)和U卡尔曼滤波(UKF)3种算法进行了分析。通过分析3种算法各自的特点,将PF算法和UKF算法的优点相结合,提出了一种新的粒子滤波算法——U粒子滤波(UPF)算法,并将其应用于GPS/DR组合导航系统中。通过对UPF算法与PF算法在GPS/DR组合导航系统中的仿真研究比较,进一步证实了UPF算法的可行性及计算的精确性。     

基于网格分块索引的车辆高频活动区域算法

车辆的高频活动区域对于车辆的服务应用有较大的参考意义,而汽车高频活动区域的计算中会基于其采集模块提供的经纬度数据进行运算,而该采集模块的经纬度数据误差通常为10米左右,较大误差的引入会影响高频区域计算的复杂度,准确性,及时性。若要降低该误差通常可借助提高采集模块的采集精度,或第三方差分定位服务,但同时导致成本上升。本文将基于当前车辆采集模块的经纬度数据精度,采用地图网格化分块索引的方式改进了车辆高频活动区域的算法,提高了车辆高频活动区域的计算的速度和准确性。     

The Use of GPS Based Velocity Measurements for Measurement of Sideslip and Wheel Slip

Summary This paper details a novel method for measuring three key vehicle states – wheel slip, body sideslip angle, and tire sideslip angle – using GPS velocity information in conjunction with other sensors. Based on initial noise data obtained from the system components, a prediction of the accuracy of the angle measurements is obtained. These results demonstrate that the errors due to stochastic noise in the GPS signal are below one degree for meaningful vehicle speeds and approach a tenth of a degree at highway speeds. Hence the limiting factor for measuring these states is not the GPS receiver, but the manner in which other implementation issues – such as bias elimination, off-axis dynamics and dead-reckoning during loss of satellite visibility – are handled. Subsequent experiments validate both the error analysis and the methodology for obtaining the measurements. The experimental results for this preliminary implementation of GPS-based state estimation compare favorably to theoretical predictions, suggesting that this technique has potential for future implementation in vehicle diagnostic and, ultimately, safety systems.     

Effect of vehicle model on the estimation of lateral vehicle dynamics

A methodology is presented for estimating vehicle handling dynamics, which are important to control system design and safety measures. The methodology, which is based on an extended Kalman filter (EKF), makes it possible to estimate lateral vehicle states and tire forces on the basis of the results obtained from sinusoidal steering stroke tests that are widely used in the evaluation of vehicle and tire handling performances. This paper investigates the effect of vehicle-road system models on the estimation of lateral vehicle dynamics in the EKF. Various vehicle-road system models are considered in this study: vehicle models (2-DOF, 3-DOF, 4-DOF), tire models (linear, non-linear) and relaxation lengths. Handling tests are performed with a vehicle equipped with sensors that are widely used by vehicle and tire manufacturers for handling maneuvers. The test data are then used in the estimation of the EKF and identification of lateral tire model coefficients. The accuracy of the identified values is validated by comparing the RMS error between experimentally measured states and regenerated states simulated using the identified coefficients. The results show that the relaxation length of the tire model has a notable impact on the estimation of lateral vehicle dynamics.     

Using GPS with a model-based estimator to estimate critical vehicle states

This paper demonstrates a method to estimate the vehicle states sideslip, yaw rate, and heading using GPS and yaw rate gyroscope measurements in a model-based estimator. The model-based estimator using GPS measurements provides accurate and observable estimates of sideslip, yaw rate, and heading even if the vehicle model is in neutral steer or if the gyro fails. This method also reduces estimation errors introduced by gyroscope errors such as the gyro bias and gyro scale factor. The GPS and Inertial Navigation System measurements are combined using a Kalman filter to generate estimates of the vehicle states. The residuals of the Kalman filter provide insight to determine if the estimator model is correct and therefore providing accurate state estimates. Additionally, a method to predict the estimation error due to errors in the estimator model is presented. The algorithms are tested in simulation with a correct and incorrect model as well as with sensor errors. Finally, the estimation scheme is tested with experimental data using a 2000 Chevrolet Blazer to further validate the algorithms.