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学院毕业设计外文翻译学生姓名 学院名称机电工程学院专业名称机械设计制造及其自动化指导教师 年05月27日 基于CMOS相机的智能汽车道路识别摘要近几年，智能辅助驾驶和导航越来越受到人们的关注，本文设计开发了一种以CMOS相机作为传感器的智能车道路识别系统，它可以完成道路识别和智能车导航功能，并说明了CMOS相机的安装和采样过程。本文设计开发了一套道路PC监控系统和道路识别算法测试程序，它可以保证道路识别的精确性、快速性和自适应性。一旦算法通过测试，那么该程序无需修改就可在嵌入式发展环境下直接应用，也可在智能车微控制器上直接应用。本文在PC机上设计开发了一个3D道路模拟系统，它很容易为道路识别系统的模拟和测量建立各种道路轨迹。此外，各种实际道路同样也可以在模拟系统上仿真。实验表明在这样的测试环境下，道路识别运算法在道路识别和路径跟踪是令人满意的。本文研究可以丰富智能车道路识别算法的研究，也为发展视觉导航和无人驾驶提供支持。关键词：CMOS照相机；智能车；道路识别；视频采样；道路模拟；算法测试1.系统介绍A 道路识别的背景基于照相机一辆无人驾驶车的概念包括一个高度自动化认知和控制技术的新兴语系，最后针对出租车常客为汽车体验用户。连同其它的发展,它们一起被很多人视为是2020年车辆的主要技术进展。道路识别是智能车交通感知和自主驾驶的前提，同时在机械视觉系统和智能导航领域被研究。许多系统通过摄像机能实现车辆的无人驾驶。THMR-V（清华移动机器人V）是一个能完美执行在平坦道路上，并加速到150千米/小时的系统。然而，许多系统需要实际信息道路或静态形象道路来测试无人驾驶的功能。在线测试的过程成本是昂贵的，因此低成本的CMOS相机模块非常的适用于汽车工业，我们的目标是开发一个可扩展的调试平台，用于研究和开发基于CMOS照相机的机械视觉和自身引导系统。通过这个系统，现实的公路电影可以用来测试道路识别功能；同时，结构化和非结构化的路都道路也可以模拟，开发人员可以修改虚拟CMOS相机的采样参数以便于调试和验证运算法则在道路识别功能中的重要性。道路识别设备和稳定的算法是提高智能车稳定性的关键。在这里,飞思卡尔16位单片机”MC9S12DG128B”(缩短为“S12 )作为主机控制器。由于S12的运算速度和记忆能力远低于电脑，所以采用640 x 480分辨率的黑白CMOS相机作为智能车辆的视频传感器。相较于其他路面传感器，CMOS相机具有快速采集的能力，这为智能车提供了足够的道路信息。详细的参数指定表I。通过数据的收集和计算，车辆能够快速确定驾驶道路。图I展示怎样安装CMOS相机和框架。B 硬件设计与抽样算法智能车上的视频采样模块由CMOS相机、视频同步分离LM1881以及S12的模数转换模块组成。表格I CMOS传感器参数模型参数图像传感器1 / 3英寸OmniVision CMOS有效性像素分辨率：640*480水平定义32视角64度频率50赫兹电源直流 9V/100mA智能车辆视角范围CMOS相机图1.CMOS相机的安装由于CMOS相机需要9伏电源, 高过车电池电压，所以采用电源转换器MC34063使照相机正常工作。在安装好相机后，需要采样的视频信号。这里LM1881视频同步分离器的使用，为智能车从视频信号控制器提取定时信息。当定时信号从LM1881芯片出现时，通过智能控制器内部的模数转换模块，智能控制器就能采样信号。采样信号通过S12芯片内部的道路识别法则来处理，轨迹表面上的黑色线路用来跟踪和分析，如图2所示。CMOS相机的检测频率为50赫兹，视频信号总是在20毫秒自转一周，用来满足高速运行的需要和实时的处理。C 模拟调试的方法由于智能车是一个实时系统，当高速运行时，调试的方法是有限的。同时，潜在的定位问题也是难解决的。为了解决这个问题，需要在PC机上建立测试模拟系统，通过智能车算法的动态库，调试过程就可以很简单的完成。用C语言编写的代码语言，它具有如下优点：1.C代码可以很容易地适用到许多类型的计算机,使软件开发的微控制器系统并联硬件设计。2.即使平台改变，C语言编写的程序也可以直接移植。3.C代码易于调试。基于C语言的可移植性，一旦该算法通过测试，它就可以不需要修改而直接满足嵌入式发展环境，然后产生的目标代码就能在微控制器上正常运行。图3中讲解了模拟调试的方法，运算库是微软Visual C+的一个工具，通过收集，一个能通过仿真测试环境的测试和验证动态链接库就产生了。稳定的算法可以被移植，然后最终运行于智能车的微控制器中。完成一个采用字段行计数足够排采样信号样本点纵信号结束行计数器+初始化结束行信号准备行信号中断场信号中断图2.CMOS采样流程D 模拟测试系统的实现仿真试验系统是由两个主要功能模块：实时监控模块和离线三维道路模拟模块组成的。这两个模块有相同的运算库,所以这一算法可以测试于在线和离线状态。1） 实时监控模块为了解S12单片机取样内部数字模拟模块和道路调试识别算法的结果，提出了一种基于建立电脑监控程序，并从COM端口或无线模块读取视频数据和回归到二维灰度图像然后显示在屏幕上的方法，如图4所示。智能车微控制器嵌入式开发环境电脑上的运算法则库动态连接库模拟测试环境图3.仿真调试的方法图4. 实时监测与调试模块监控模块的原理是这样的：当通信配置完成时，程序初始化开始检测。当智能车得电，并且通过无线检测模块发送一个包括行和列的视频信号数据包给电脑监控程序，监控程序然后评估收到参数的当前价值。因此，建立了智能车和电脑之间的通信通道，智能车运行时发出的CMOS信号可以显示在屏幕上进行监测。获得的视频数据也可以用来模拟道路信号，并且同时可以在电脑上，可以测试智能车的道路算法，参阅图4-道路识别结果，这样提高了开发效率,确保了算法的准确性和适应性。运算法则消耗的时间是合适和有效的，这样保证了该算法可以快速处理大量数据。一旦该算法通过测验,它可以不经修改而直接满足嵌入式发展环境,并在S12芯片正常运行。实验证明了智能车在道路识别算法产生的结果和在电脑模拟的结果是一样的。这也显示了道路识别算法在进行仿真调试的优势。流程图5显示了监测和调试模块。显示运算法则测试结果其它运算法则速度控制稳定控制道路识别结束运算法则测试记录一帧记录影像读取数据并显示初始化视频数据包连接算法法则库图5. 实时监测模块处理资料2） 三维道路模拟模块在线测试是检验算法的最好方法，但是包括两大耗费问题：第一，对整车应该在一个可行的条件下,从机械部件到电子设备和传感器软件都要正确。第二，要求多种团队成员，后勤是成本的一部分同时在这个过程中花费时间。此外，为了使智能车的道路识别算法适应任意道路，通过在电脑上建立离线三维道路模拟和测量系统，使用一套三维图像引擎生成真实感较强的三维图像，实际上在轨道内取样，通过S12芯片的数字模块转换成模拟分布在不同道路的视频信号。仿真流程图如图6所示。当仿真系统启动,根据参数预案创建智能车的模拟场景。然后系统进入一个循环以显示和放置所有现场目标。在这个循环,CMOS相机实际上是采样虚拟智能车；然后采样数据作为函数调用参数进入了算法库。在算法库中，公认了道路的轨迹，同时计算了汽车的转向装置和驱动马达的数值。这些结果都被归还库用于模拟智能车动作和显示。道路识别和其它算法可以通过信号采样来测试。任何一种赛道比如曲线公路，十字路口和斜坡公路都可以模拟，同时智能车不仅可以通过软件控制，而且还可以用键盘控制。各种各样的问题被检测出来并解决，这样加快了道路识别算法的测试过程。实际的路面也可以进行模拟，并通过提供决策支持系统来发展视觉导航和自主驾驶。图7是仿真系统的模拟。三维实时仿真技术现广泛应用于测试智能车的道路识别算法。自从轨迹的仿真场景很容易被定制，许多算法包括道路识别算法可以进行全面试验，当实际道路不存在，潜在的问题也可以被发现。系统初始化创建一个场景设计智能车初始化运算库循 环CMOS相机虚拟采样道路识别连接运算库稳定控制电机控制其它算法显示算法测试结果记录影像记录一帧显示全部画面系统终结图6. 资料处理的三维仿真模块图7. 智能车和三维仿真调试E 道路识别算法道路识别是智能车主要的引导系统。在仿真系统环境中，解释了基于智能车的一个有效道路识别运算法则。跑道是黑色的特征线铺在白色道路上,并在白色道路上，黑线平行于道路边界。任何道路形态，如直路、十字路和斜坡路都是适应的。智能车的目标是检测识别黑色特征线并使车高速跟随它。我们通过CMOS相机来处理大部分二维图像。动态临界值应用于边缘提取，来消除道路亮度变化的影响。灰度图像从底线扫描到顶线；每一行返回一个黑点,（黑点）体现了（小车）在轨道上的位置。下一个黑点的位置在前一个黑点的范围之内，所以没有必要因为临界值去对照线上的每一个像素。流程图如图8所示。该算法对仿真测试系统和智能车在真实路面上进行了测试。虽然在路面识别上并没有复杂的计算；而然，智能车对于跟随黑色的特征行驶是非常快速和正确的。结束处理开始线处理科斯线在范围内搜索黑点测定最近行的搜索范围当前行-当前行=0当前行=最近行寻找黑点最近行寻找阀门最近行模块进入图8.智能车的路面识别算法2.结果道路识别系统应用于智能车上，解决了车辆道路识别和无人驾驶的功能。基于道路识别监视器、3D道路仿真和系统测试的电脑已经被应用于测试道路识别算法。实验表明，在这样的实验中，道路识别算法在道路识别和追踪上是令人满意的，同时智能车的平均速度能到达2.3m/s，如图9所示。图9.安装了CMOS相机的智能车3.展望在2002年, 宣布了达帕尔大挑战赛车比赛项目，比赛允许国际队伍采用完全自主的车辆参加，并在崎岖不平的土路和无人居住的地方进行比赛。到目前为止这样的比赛已经成功的举办了六年。在比赛中，除了雷达和其它传感器，数码相机被广泛应用。虽然，在任意恶劣的环境中能安全行驶的目标并未达成，但是安装摄像机用于辅助驾驶是未来的趋势。自从我们的3D仿真系统能模拟道路表明的每个实物并使智能车能在上面行驶，因此对研究智能车在道路辨识职能的改良，以及为发展提供支持。Intelligent Vehicle Road Recognition Based on the CMOS CameraChu Liu*, Jie Chen*, Yifan Xu* and Feng Luo* * College of Automotive Engineering, Tongji University, Shanghai, China. Email: * College of Automotive Engineering, Tongji University, Shanghai, China. Email: panggebiao * College of Automotive Engineering, Tongji University, Shanghai, China. Email: freeskyflying * College of Automotive Engineering, Tongji University, Shanghai, China. Email: luo_fengAbstractSince the problems of intelligent auxiliary driving and co-navigating have received more and more attention recent years, a Road Recognition System is developed for the Intelligent Vehicle with CMOS camera as its road sensor, which provides solutions for the Road Recognition and automatic drive functions of the Intelligent Vehicle. The installation and sampling process of the CMOS camera is explained. A PC based monitor and test program of the Road Recognition Algorithm is build to guarantee the accuracy, rapidity and adaptability of the road recognition function. Once the algorithm passes the test, it can be compiled directly under embedded development environment without modification and runs in the micro controller of the Intelligent Vehicle properly. A 3D road simulation system is also build on PC, which easily creates all kinds of tracks for the emulation and measurement of the road recognition system, besides, each kind of the actual road can also be emulated by the simulation system. Experiments prove that under such tests, the Road Recognition Algorithm is satisfying for road recognition and tracking, so the approach could actively improve the research on Road Recognition function of Intelligent Vehicle, and also provides support for the development of vision navigation and autonomous driving.Keywords CMOSCamera IntelligentVehicle RoadRecognition Video Sampling Road Simulation Algorithm TestI. SYSTEM INTRODUCTIONA. The Background of Road Recognition based on the CameraThe driverless car concept embraces an emerging family of highly automated cognitive and control technologies, ultimately aimed at a full taxi-like experience for car users, but without a human driver. Together with alternative propulsion, it is seen by some as the main technological advance in car technology by 2020. Road Recognition is the premise of traffic perception and autonomous driving of the Intelligent Vehicle, which is also studied in the field of machine vision and intelligent navigation. Many systems have been developed which can drive autonomously using video cameras. THMR-V (Tsinghua mobile robot V) is a system that performs well with a speed up to 150 km/h in structured road 1.However, many of the systems require actual road information or static road image for the test of its autonomous driving function. The cost of on-line test process may be expensive. Since low cost CMOS camera modules are ideal for many automotive applications, our goal was the development of an extendable debugging platform for the research and development of machine vision and self-piloting based on the CMOS camera. With this system, real road movie can be used to test the road recognition function; both structured road and unstructured road can also be simulated 2, developers are able to modify the sampling parameters of the virtual CMOS camera so as to debug and validate the important algorithm in the road recognition function.Road Recognition devices and stable algorithm are the key to improving the stability of the Intelligent Vehicle. Here the Freescale 16-Bit micro controller MC9S12DG128B (shorter from S12) is used as the core controller on the vehicle 3. Since the operation speed as well as memory capacity of S12 is much lower than PC, the Black & White CMOS camera with resolution 640 x 480 is taken as the video sensor for the Intelligent Vehicle. Compared with other road sensors, CMOS camera possesses the ability of fast-collecting and forward-looking, which also provides enough road information for the Intelligent Vehicle. The detailed parameters are specified in Table I. With the data being collected and calculated, the vehicle is able to determine the track itself for fast driving. Fig. 1 shows installation of CMOS Camera and framework.B. Hardware Design and Sampling AlgorithmThe video sampling module of the intelligent vehicle is composed of CMOS Camera, LM1881 video sync separator and ADC module of S12. Since the CMOS camera requires 9 Volts power supply, which is higher than the vehicle battery voltage, the DC-DC converter MC34063 is used to make the camera work properly. TABLE I. CMOS SENSOR PARAMETERSAfter the installation of the camera, the sampling of the video signal is required. Here the LM1881 video sync separator is used, which extracts timing information from the video signal for the vehicle controller. Then the vehicle controller is able to sample the signal with its internal ADC module when the timing signal from LM1881 occurs. The signal sampled is then processed by the road recognition algorithm inside S12, the black line marker on the surface of the track is detected and analyzed, as shown in Fig. 2. The detection frequency of the CMOS camera is 50Hz, the video signal is processed within 20 milliseconds, which satisfies the needs for high-speed running and real-time processing.Figure 1. Installation of CMOS CameraC. The Method of Simulation DebuggingSince the Intelligent Vehicle is a real-time system, while running at a high speed, the debugging method is limited. Locating a potential problem at the same time can be difficult. In order to solve this matter, a simulation test system based on PC is built, with all the algorithm of the Intelligent Vehicle implemented in its dynamic library, the debugging process can easily be fulfilled. The code is written in C Language, which has the following advantages: (1) C code can easily be realized on many kinds of computers, which makes the software development of micro controller system in parallel with hardware design. (2) Programs written in C Language can be transplanted directly when the platform is changed. (3) C code is easy for debugging.Based on the portability of C Language, once the algorithm passes the tests, it can be compiled directly under embedded development environment without modification and then the generated target code is able to run in micro controller properly. Fig. 3 explains the method of simulation debugging, the algorithm library is implemented in Microsoft Visual C+ 6.0, after compile, a dynamic link library is generated, which is referenced by the simulation test environment for the test and validation. Stable algorithm can then be transplanted and finally runs in the micro controller of the Intelligent Vehicle.Figure 2. CMOS Sampling Flow ChartD. The Implemention of the Simulation Test SystemThe Simulation Test System is made up of two main modules, real-time monitor module and off-line 3D road simulation module. These two modules share the same algorithm library, so the algorithm can be test thoroughly both on-line and off-line.1) Real-time Monitor ModuleIn order to know the sampling result from the S12 internal Analog-to-digital module and to debug the Road Recognition Algorithm, a PC based monitor program is build, which reads the video data from Serial COM Port or wireless module and reverts it to two-dimensional gray-scale image and then displays it on the screen in real-time, as shown in Fig. 4. Figure 3. The Method of Simulation DebuggingThe mechanism of the monitor module is as follows: When the communication configuration is completed, the program initializes and starts monitoring. The Intelligent Vehicle powers up, and sends a setup packet via wireless module to the monitor program on the PC, which includes the rows and columns of the video signal. The monitor program then sets receive parameters to the current value. Thus, a communication channel is established between the Intelligent Vehicle and PC. After that, the CMOS signal sent by the Intelligent Vehicle while running can be displayed on screen for monitoring. The video data acquired can also be used to simulate the road signal and test the Intelligent Vehicles Road Recognition Algorithm at the same time on the PC, see Fig. 4 - Road Recognition Results, which improves the development efficiency, guarantees the accuracy and adaptability of the algorithm. Time consumed by the algorithm is calculated and displayed accordingly, which guarantees that the algorithm is rapid enough to process a large amount of data. Once the algorithm passes the tests, it can be compiled directly under embedded development environment without modification and then runs in S12 properly. Experiments proof that, the results generated by the Road Recognition Algorithm on the Intelligent Vehicle and the results from PC simulation are identical. This also shows the advantages of the Road Recognition Algorithm simulation debugging. Fig. 5 shows the flow chart of the monitoring and debugging module.Figure 4. Real-time Monitoring and Debugging Module2) 3D Road Simulation ModuleOn-field testing is the best way to test the algorithm, but it involves two major overheads, firstly the entire vehicle should be in a workable condition, right from the mechanical components to the electronics, and the sensors to the software. Secondly, requirement of multiple team members, the cost involved in the logistics and time spent in the process 6. Besides, in order to make the Intelligent Vehicles Road Recognition Algorithm adapt arbitrary tracks, an off-line 3D road simulation and measurement system is build on PC, which uses OpenGL graphics engine to generate 3D scenes, the tracks inside are virtually sampled and converted to simulate the video signal sampled by S12 analog-to-digital module. The simulation flow chart is shown in Fig. 6. When simulation system starts, the simulated scene including the Intelligent Vehicle is created according to the parameters pre-defined. Then the system enters a loop to display and position all scene targets. In this loop, the CMOS camera over the simulated Intelligent Vehicle is virtually sampled; the sampled data is then taken as parameters for the function call of algorithm library. Figure 5. Data Processing of Real-time Monitor ModuleIn the algorithm library, the path is recognized, and the steering gear as well as motor drive value is calculated. All of these results are returned by the library for the motion and display of the simulated Intelligent Vehicle. Road Recognition and other algorithm can then be tested by the signal virtually sampled. Any kind of race track such as road curve, road crosses and slopes can be simulated, and the virtual Intelligent Vehicles can be controlled on the software interface not only by the algorithm library but also by the keyboard. Thus various problems are detected and solved, which speeds up the testing process of the Road Recognition Algorithm. Actual road surface can also be simulated by the system, which provides support for the development of vision navigation and autonomous driving. Fig. 7 is the interface of the simulation test system.The technique of 3D real-time simulation is now widely applied in the test of Road Recognition Algorithm of the Intelligent Vehicle. Since the tracks in the simulated scene are easily customized, many algorithms including Road Recognition Algorithm can be tested thoroughly and potential problems can be found when the actual roads are not present. E. Road Recognition AlgorithmFigure 6. Data Processing of 3D Simulation ModuleRoad Recognition is the major task of autonomous vehicle guidance. Here an efficient road recognition algorithm based on the intelligent vehicle is explained, which is developed under the simulation test system. The track is made up of black feature lines over white road surface, which is in the middle of the road, parallel to the road boundaries. Many road conditions such as straight roads, road crosses and slopes can be shaped. The goal of Intelligent Vehicle Road Recognition is to detect the black feature lines and make the vehicle follow them at a high speed. We deal mainly with the two-dimensional gray-scale image sampled by the CMOS camera. Dynamic threshold is applied for the edge extraction, which avoids the impactof changes in brightness of the road. The gray-scale image is scanned from bottom line to top line; each line returns a black point, which represents the position from the track. The next black point is within the range of the former point, so there is no need for the threshold c
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