MEMS传感器在车辆运动状态监测系统中的应用

针对智能车辆辅助驾驶系统对车辆运动状态监测的要求,提出了采用MEMS传感器对驾驶人员操纵车辆进行实时监测。该方法具有较好的实时性、智能性。为提高汽车行驶性和安全性提供重要的理论和实践依据。     

驾驶操作数据采集系统设计研究

设计了驾驶操作数据采集系统。该系统包括传感器和驾驶动作记录仪两部分。根据传感器选型和布设的原则,结合需要采集的车辆状态和操纵机构动作信息,对传感器进行了设计;并从硬件、主程序设计两方面对驾驶动作记录仪进行了设计。通过在实车搭建驾驶操作数据采集系统采集数据,验证了系统的可行性。     

基于MEMS传感器的车载微惯性测量单元设计

为了感知汽车姿态,利用MEMS传感器自主设计了一种车载微惯性测量单元(MIMU),详细介绍了三轴MEMS加速度计、三轴MEMS陀螺和正六面体的设计。该单元具有成本低、精度高、体积小,且便于标定等优点,可以方便地运用到汽车姿态实时监测系统中。给出了单元的标定方法,分析了其误差来源,建立误差模型以及自主设计了标定系统。实验结果表明:该单元能有效地消除信号干扰,具有满意的精度要求,可对运用该单元进行汽车姿态监测等提供理论研究与工程应用参考。     

基于全方位视觉传感器的驾驶疲劳视频检测装置的设计

针对驾驶疲劳问题,设计了一种基于全方位视觉传感器的驾驶疲劳视频检测装置;首先使用全方位视觉传感器采集到驾驶员脸部、方向盘和道路环境的视频信息,接着采用图像识别手段检测各种能反应驾驶员疲劳的因素,如驾驶员的感知疲劳、判断疲劳和动作疲劳,最后根据以上所检测到的信息综合判断驾驶员是否处于驾驶疲劳状态;实验结果表明,该检测装置能有效地减少驾驶疲劳的漏判率和误判率。     

基于模糊推理的汽车辅助驾驶系统控制算法

在建立汽车辅助驾驶系统模型的基础上，指出满足驾驶员的驾驶特征是车辆控制的一个重要指标，此外针对驾驶员驾驶行为的不精确性，提出了以模糊推理为基础的上位控制方法，并对其进行了现场实验。实验结果表明，用模糊控制理论模拟驾驶行为的不精确性是可行的。通过模糊控制自车的速度，能够实现自车在多种工况下保持安全状态。     

基于GPS/IMU数据的汽车转向角重现方法研究

汽车操纵角是表征驾驶员控制汽车的最主要的参数之一。利用行驶记录仪(汽车黑匣子)的GPS和惯性传感器数据,重现驾驶员的操纵过程,对于事故分析、驾驶员行为分析等应用都有积极的意义。根据汽车单轨模型,提出了基于汽车行驶速度和加速度的转向角的计算方法。用汽车动力学仿真和实车行驶等实验,证明方法的可行性和可靠性。计算结果受汽车侧倾和侧偏两种现象的影响,经误差分析可知,在一般的转向行为中(时速35 km/h内),计算结果的相对误差在10%以内。     

基于嵌入式系统汽车行驶记录仪的研制

为了遏止疲劳驾驶、车辆超速等交通违章、约束驾驶人员的不良驾驶行为、保障车辆行驶安全,设计了一种以AVR处理器为核心μC/OS-Ⅱ为操作系统的汽车行驶状态记录仪系统,串行DSI302为实时时钟,大容量串行K9F6408Flash存储器为存储介质,对汽车行驶状态进行实时监测和记录,系统可以通过串口或USB接口进行参数设置和记录数据的转储,转储到PC机中的数据通过上层分析软件进行分析处理,从而为主管部门对车辆及驾驶员的管理和调度提供科学的依据,实验表明该记录仪完全符合国家标准.     

基于卷积神经网络的驾驶辅助系统设计

为了减轻驾驶员在行驶过程中的操作负担,进而降低误差判断事件的出现几率,设计一种基于卷积神经网络的驾驶辅助系统。在执行良好的汽车导航架构中,限定Learning Navigation模块与Learning Controller模块的连接位置,再根据辅助驾驶传感器对于行驶画面的采集情况,对车辆巡航能力进行定向控制,抑制监测仪表中辅助波的过渡振动,完成驾驶辅助系统的需求与设计分析。在此基础上,确定辅助激活函数、约束仪表中的行车图像,建立标准化的卷积神经网络。按照驾驶辅助数据的学习结果,对其进行传输处理,进而连接驾驶辅助系统的Job请求,实现系统的顺利运行。利用卷积神经网络平台设计实车实验结果表明,应用驾驶辅助系统后,车辆监测仪表中辅助波振动幅度的最小值处于36-61Hz之间,平均波长偏移量明显减小,驾驶员的行驶操作负担得到有效缓解。     

基于OBDII&EOBD的机动车监测系统分析与设计

针对国内个人用户、行业用户对实时性车辆状态、驾驶习惯和故障检修的需求,设计了一套机动车实时监测系统。基于OBDII&EOBD协议,车载OBD终端设备获取车辆故障码、地理位置、行车速度等数据信息,通过GPRS与基于J2EE架构的网站服务端建立通信。服务端对数据进行分析,从而对车辆的故障状态进行诊断,统计归纳驾驶员的驾驶习惯和行车状况,最终将车辆检测和统计分析的结果展现在网页客户端,或者i OS/Android移动手持设备上。视车辆的故障情况,驾驶员可以选择通过本系统与汽车维修厂建立联系,为驾驶员的行车安全、出行便利,以及交通管理和保险业提供参考建议和数据支持。     

基于MEMS的可穿戴无线体域网设计与实现

针对高精度低成本可穿戴式人体动作捕捉,采用MEMS微惯性传感单元(IMU)设计实现无线体域网系统.系统由六轴微惯性单元MPU-6050、三轴地磁仪AK8975、微处理器及射频模块CC2530构成.首先,详细讨论系统硬件构架和基于Z-Stack的软件构架,然后通过上位机Matlab用户程序进行数据采集与实验验证.实验结果表明,与其他微惯性传感器系统相比,本设计采用高集成度的芯片和无线通信方式,在减小硬件体积的同时能够获得较高精度的数据(角度传感器零漂＜1.5°),对实现可穿戴无线惯性测量有着积极的作用.     

电动车驾驶机器人控制器参数自学习方法研究

针对人工驾驶和现有的机械式驾驶机器人在电动车续驶里程试验中存在成本高、耗时长和错误率高的问题,利用电动车结构和控制上的独特性,依托自行设计的电动车转毂驾驶机器人,采用电信号完成对车辆的控制。为了提高系统适应性,采用非线性最小二乘法实现电动车模型参数的在线辨识;并基于粒子群算法对车辆PID控制器参数进行在线整定,从而完成对任意设定工况速度曲线的跟随。实车试验表明,提出的方法能实现加速踏板的平滑控制,且控制重复性高,完全可以代替驾驶员进行电动车续驶里程试验。     

Role of lateral acceleration in curve driving: driver model and experiments on a real vehicle and a driving simulator

Experimental studies show that automobile drivers adjust their speed in curves so that maximum vehicle lateral accelerations decrease at high speeds. This pattern of lateral accelerations is described by a new driver model, assuming drivers control a variable safety margin of perceived lateral acceleration according to their anticipated steering deviations. Compared with a minimum time-to-lane-crossing (H. Godthelp, 1986) speed modulation strategy, this model, based on nonvisual cues, predicts that extreme values of lateral acceleration in curves decrease quadratically with speed, in accordance with experimental data obtained in a vehicle driven on a test track and in a motion-based driving simulator. Variations of model parameters can characterize "normal" or "fast" driving styles on the test track. On the simulator, it was found that the upper limits of lateral acceleration decreased less steeply when the motion cuing system was deactivated, although drivers maintained a consistent driving style. This is interpreted per the model as an underestimation of curvilinear speed due to the lack of inertial stimuli. Actual or potential applications of this research include a method to assess driving simulators as well as to identify driving styles for on-board driver aid systems.     

Method for the analysis of posture and interface pressure of car drivers

Biomechanical study of car driver posture is one of the most referenced aspects for the ergonomic design process of the whole vehicle. The aim of this work is to present a multi-factor method for the analysis of sitting posture and the resulting interactions of the car driver body with the cushion and the backrest. The proposed method, based on the combined use of an optoelectronic system for motion capture and suitable matrices of pressure sensors, has allowed the measurement of a large set of car driver posture parameters and the identification of specific sitting strategies characterising the driving posture, despite the different behaviours of the analysed subjects.     

Driving situation recognition with uncertainty management and rule-based systems

The recognition of a temporal sequence is a complex problem, especially in the framework of driving situations. However, this recognition is essential for the development of driving assistance systems. This paper presents a rule-based system that manages the real-time measurements got from sensors of an experimental vehicle, in order to determine the current possible maneuvers worked out by the driver. The particularity of the proposed system is that it manages the inaccuracy of the data and the uncertainty of the recognition, using fuzzy subsets and beliefs on hypotheses.     

Modeling and prediction of driver behavior by foot gesture analysis

Understanding driver behavior is an essential component in human-centric Intelligent Driver Assistance Systems. Specifically, driver foot behavior is an important factor in controlling the vehicle, though there have been very few research studies on analyzing foot behavior. While embedded pedal sensors may reveal some information about driver foot behavior, using vision-based foot behavior analysis has additional advantages. The foot movement before and after a pedal press can provide valuable information for better semantic understanding of driver behaviors, states, and styles. They can also be used to gain a time advantage in predicting a pedal press before it actually happens, which is very important for providing proper assistance to driver in time critical (e.g. safety related) situations. In this paper, we propose and develop a new vision based framework for driver foot behavior analysis using optical flow based foot tracking and a Hidden Markov Model (HMM) based technique to characterize the temporal foot behavior. In our experiment with a real-world driving testbed, we also use our trained HMM foot behavior model for prediction of brake and acceleration pedal presses. The experimental results over different subjects provided high accuracy (～94% on average) for both foot behavior state inference and pedal press prediction. By 133 ms before the actual press, ～74% of the pedal presses were predicted correctly. This shows the promise of applying this approach for real-world driver assistance systems.     

A path planning and path‐following control framework for a general 2‐trailer with a car‐like tractor

Maneuvering a general 2‐trailer with a car‐like tractor in backward motion is a task that requires a significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path‐following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice‐based path planning framework is developed in order to generate kinematically feasible and collision‐free paths and a path‐following controller is designed to stabilize the lateral and angular path‐following error states during path execution. To estimate the vehicle state needed for control, a nonlinear observer is developed, which only utilizes information from sensors that are mounted on the car‐like tractor, making the system independent of additional trailer sensors. The proposed path‐planning and path‐following control framework is implemented on a full‐scale test vehicle and results from simulations and real‐world experiments are presented.     

Driving Profile Modeling and Recognition Based on Soft Computing Approach

Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.     

矿井信集闭系统中车载式机车定位传感器

为解决以往煤矿井下运输信集闭系统中依赖轨道铺设的传感器实现机车位置监测的可靠性差的难题,本文提出一种不依赖轨道铺设传感器的机车位置监测的煤矿铁路运输信集闭系统,采用捷联惯性导航技术,设计了依靠电机车自身精确定位的信集闭系统。重点介绍捷联惯性导航系统的算法问题以及车载式机车定位的硬件设计方案,经实验测试,取得了良好的效果。     

A Computer Vision System for Lateral Localization

For urban driving, knowledge of ego‐vehicle's position is a critical piece of information that enables advanced driver‐assistance systems or self‐driving cars to execute safety‐related, autonomous driving maneuvers. This is because, without knowing the current location, it is very hard to autonomously execute any driving maneuvers for the future. The existing solutions for localization rely on a combination of a Global Navigation Satellite System, an inertial measurement unit, and a digital map. However, in urban driving environments, due to poor satellite geometry and disruption of radio signal reception, their longitudinal and lateral errors are too significant to be used for an autonomous system. To enhance the existing system's localization capability, this work presents an effort to develop a vision‐based lateral localization algorithm. The algorithm aims at reliably counting, with or without observations of lane‐markings, the number of road‐lanes and identifying the index of the road‐lane on the roadway upon which our vehicle happens to be driving. Tests of the proposed algorithms against intercity and interstate highway videos showed promising results in terms of counting the number of road‐lanes and the indices of the current road‐lanes.