CN102390320A - Vehicle anti-collision early warning system based on vehicle-mounted sensing network - Google Patents

Vehicle anti-collision early warning system based on vehicle-mounted sensing network Download PDF

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CN102390320A
CN102390320A CN2011102404947A CN201110240494A CN102390320A CN 102390320 A CN102390320 A CN 102390320A CN 2011102404947 A CN2011102404947 A CN 2011102404947A CN 201110240494 A CN201110240494 A CN 201110240494A CN 102390320 A CN102390320 A CN 102390320A
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vehicle
module
collision
information
time
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CN102390320B (en
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严新平
张存保
钟益萍
陈超
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武汉理工大学
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Abstract

The invention relates to a vehicle anti-collision early warning system based on a vehicle-mounted sensing network. The vehicle anti-collision early warning system comprises an subject vehicle (SV) information module, a self-learning functional module, an information memory module, a vehicle collision judging module, an anti-collision early warning device module, a DSRC (Dedicated Short Range Communications) vehicle-vehicle communication module and a neighborhood vehicle (NV) anti-collision early warning system, wherein the SV information module consists of an information acquiring and processing module and a module including the ID (Identity) and the size fixed information of a vehicle; the information acquiring and processing module is used for exchanging data with the anti-collision early warning device module through the self-learning functional module and the vehicle collision judging module; the module including the ID (Identity) and size fixed information of the vehicle is used for exchanging data with the anti-collision early warning device module through the information memory module and the vehicle anti-collision judging module; and the information memory module is in DSRC wireless communication with the NV anti-collision early warning system of a surrounding vehicle through the DSRC vehicle-vehicle communication module. By adopting the vehicle anti-collision early warning system, the vehicle running state can be forecast, and the effectiveness and suitability of the anti-collision early warning system are enhanced.

Description

基于车载传感网络的车辆防撞预警系统 Based vehicle collision warning systems on-board sensor networks

技术领域 FIELD

[0001] 本发明涉及车辆防撞预警系统,特别是一种基于车载传感网络的车辆防撞预警系统。 [0001] The present invention relates to a vehicle collision warning systems, particularly to a vehicle-based sensor network vehicle collision warning systems.

背景技术 Background technique

[0002] 随着汽车数量的快速增长,车辆碰撞事故也呈上升趋势,导致大量的人员伤亡和财产损失。 [0002] With the rapid growth in the number of cars, vehicle collisions also on the rise, resulting in a large number of casualties and property losses. 为减少车辆碰撞事故,近年来,基于红外、雷达、摄像机等传感器的车辆防撞预警装置被广泛关注,其原理一般是通过传感器检测本车与其它车辆的距离,当距离值小于设定的安全阈值时,给出报警提示。 To reduce the vehicle collision accidents, in recent years, anti-collision warning apparatus for a vehicle based on an infrared, radar, sensor, camera or the like has been widespread concern that the general principles of the present vehicle is detected by the sensor from the other vehicles, when the set value is less than the safety distance threshold, alarm is given. 上述方法着眼于本车与周围车辆的相对距离和速度,所获取的信息量较少,存在一定的局限性。 The method described above looks at the relative distance and the speed of the vehicle and the vehicle surroundings, the acquired information is less, there are some limitations. 另外,红外、雷达、摄像机等传感器容易受天气、障碍物、光线等因素的影响,降低了防撞预警装置的适用性和效果。 Further, infrared, radar, cameras and other sensors susceptible to weather factors, obstacles, and other light, and the effect of reducing the applicability collision warning device.

[0003] 近年来,车载传感网络技术得到快速发展,并被应用于车辆防撞预警系统,即在车辆上安装车载传感网络设备,实现车辆之间的实时数据交换,获取车辆行驶环境信息,并在此基础上进行防撞预警。 [0003] In recent years, in-vehicle sensor network technology has been developed rapidly, and is applied to a vehicle anti-collision warning system, i.e. the sensor is mounted in-vehicle equipment in the vehicle network, real-time data exchange between the vehicle, the vehicle traveling environment information obtaining , and anti-collision warning on this basis. 申请号为200410042177. 4的专利提出了利用无线自组织网络技术实现高速公路上的汽车防撞方法,该专利用IPv6实现车辆自组织网络,将测速雷达检测的速度与警戒值速度进行比较,当检测的速度高于警戒速度时给出报警提示,该技术方案主要针对高速公路这一特定场景。 Patent Application No. 200410042177.4 proposes using MANET technology automotive collision avoidance method on the highway, with this patent achieve IPv6 vehicle ad-hoc network, the speed and the speed value of the speed warning radar detection is performed by comparing warning alarm is given when the speed is higher than the detected speed, the main aspect of this particular scene for a highway. 申请号为200810197439. 2的专利进一步利用了车载GPS和传感器模块获取本车的位置、方向、速度和加速度等信息,并与周围车辆进行数据交换,通过计算碰撞概率与概率阈值的差进行决策预警。 Application No. 200810197439.2 patent further use of the information board GPS and sensor module acquires the vehicle position, direction, speed and acceleration, and performs data exchange with the surroundings of the vehicle, make decisions warning by differential collision probability threshold for probability values ​​is calculated . 申请号为200910064025的专利设计了一种用于雾天汽车防撞的电路装置,把GPS和双轴加速度传感器的信息输入到DSP (Digital Signal I^rocessor,数字信号处理器)进行运算处理,并结合本车与周围车辆的相对距离和相对速率,适时给出声光报警。 Patent Application No. 200 910 064 025 a circuit arrangement is designed for a car crash the fog, and the GPS information is input to the biaxial acceleration sensor DSP (Digital Signal I ^ rocessor, digital signal processor) performs arithmetic processing, and binding relative distance and the relative speed between the vehicle and the vehicle surroundings, a timely alarm to sound and light. 现有利用车载无线传感器网络技术的车辆防撞预警系统存在以下不足: Existing wireless sensor network technology utilizing on-board vehicle collision warning system has the following disadvantages:

(1)未充分利用车载传感网络的优势,除了车辆速度、加速度、位置等信息外,还可引入车身偏转角、方向盘转角、油门踏板角位移、刹车踏板角位移、转向灯状态等信息,用于预测车辆的运行状态和运行轨迹,提高防撞预警的准确性和有效性。 (1) does not take advantage of the in-vehicle sensor network, in addition to the information of the vehicle speed, acceleration, position, etc., may also be introduced into the body yaw angle, steering wheel angle, angular displacement of the accelerator pedal, the angular displacement of the brake pedal, turn signal status information, for predicting vehicle running state and running track, improve the accuracy and effectiveness of collision warning.

[0004] (2)虽然有专利提出过考虑不同驾驶员性格差异对车辆防撞算法的影响,但是通过人为设定方式可能会造成与实际状况存在较大出入。 [0004] (2) Although the patent proposed to consider the impact of different personality differences driver of the vehicle collision avoidance algorithm, but by artificially setting mode may cause the actual situation there is a big discrepancy. 未有专利提出过对驾驶员实时行为特性有“自学习”功能的防撞预警系统。 No patent put forward the "self-learning" function anti-collision warning system for real-time behavior characteristics of the driver.

发明内容 SUMMARY

[0005] 本发明所要解决的技术问题是:针对现有车辆防撞预警系统存在的问题,提出一种基于车载传感网络的车辆防撞预警系统,该系统利用车载传感网络,实时交换车辆之间的当前状态信息,并对其行驶状态进行预测,同时,通过本车实时状态信息对驾驶员行为特性进行自学习来预测驾驶员的性格特点,进而判断本车与周围车辆之间是否会发生碰撞。 [0005] The present invention solves the technical problem: for the presence of a conventional vehicle collision warning systems, vehicle collision avoidance warning system is proposed based on in-vehicle sensor network, the sensor network system using the vehicle, the vehicle real-time exchange the current state of information between, and predict the state of travel, same time, self-learning behavioral characteristics of the driver of the vehicle through real-time status information to predict personality traits of the driver, and then judge whether between the vehicle and the surrounding vehicles Collision. 当可能发生碰撞事故时,向驾驶员发出预警提示,便于驾驶员及时采取措施避免碰撞事故,从而提高道路交通安全水平。 When a collision accident occurred, issued a warning to prompt driver, to facilitate the driver to take timely measures to avoid a collision, thus improving road safety.

[0006] 本发明解决其技术问题采用以下的技术方案: [0006] The present invention to solve the technical problems by the following technical solution:

本发明提供的基于车载传感网络的车辆防撞预警系统,包括本车SV (Subject Vehicle,简称SV)信息模块、自学习功能模块、信息存储器模块、车辆碰撞判断模块、防撞预警装置模块、DSRC (Dedicated Short Range Communication,专用短程通信)车车通信模块和周围NV (Neighborhood Vehicle,简称NV)车辆防撞预警系统,其中:本车SV信息模块由信息采集处理模块和包括车辆本身的ID及尺寸固定信息的模块组成,信息采集处理模块经自学习功能模块、车辆碰撞判断模块与防撞预警装置模块进行数据交换,车辆本身的ID及尺寸固定信息的模块经信息存储器模块、车辆碰撞判断模块与防撞预警装置模块进行数据交换;信息存储器模块经DSRC车车通信模块与周围车辆NV防撞预警系统实现DSRC无线通信。 The present invention provides a vehicle anti-collision warning system based on vehicle sensor network, comprising a vehicle SV (Subject Vehicle, referred to as SV) information module, self-learning module, memory module information, a vehicle collision block, collision warning device module, DSRC (dedicated Short Range communication, dedicated short range communication) communication module and a truck around NV (Neighborhood vehicle, referred NV) of the vehicle collision warning systems, wherein: the vehicle ID from the SV information module information acquisition and processing module and the vehicle itself and comprising fixed information block size of the composition, the information acquisition and processing module is self-learning module, vehicle collision avoidance warning and collision block device module data exchange, the vehicle itself module is a memory module ID information and size information of a fixed, vehicle collision block exchange data with the anti-collision warning device module; information memory module for wireless communication via DSRC-vehicle DSRC communication module around the vehicle collision warning systems NV.

[0007] 所述信息采集处理模块需要采集的信息可以分为ABS (Anti-lock Braking System,防抱死系统)车辆速度获取、DGPS (Difference Global Positioning System,差分式全球定位系统)信息采集处理和其他传感器信息采集处理三大类,其中:DGPS信息采集处理包括卫星、差分式GPS基站及车载端移动GPS模块,处理后得到的信息有车辆位置和绝对运动方向;ABS车辆速度的获取不再额外加传感器,直接从ABS控制器中把车辆速度数值输出备用;其他传感器信息采集处理包括双轴加速度传感器、车身偏转角(陀螺仪)、光电感应式方向盘转角传感器、油门&刹车踏板角位移传感器。 [0007] The information acquisition and processing module may be collected into ABS (Anti-lock Braking System, ABS) vehicle speed obtaining, DGPS (Difference Global Positioning System, Differential Global Positioning System) information collection and processing other sensor information acquisition and processing three categories, including: DGPS information collection processing including satellite, differential GPS base station and a mobile GPS car end module, processing information are obtained and the absolute position of the moving direction of the vehicle; a vehicle speed acquired at no extra ABS plus sensor, directly from the ABS controller in the vehicle speed value output standby; additional sensor information acquisition process includes a biaxial acceleration sensor, a yaw angle of the vehicle body (gyro), steering wheel angle sensor type photoelectric sensors, accelerator & brake pedal angular displacement sensor.

[0008] 所述车载端移动GPS模块可以包括射频前端、信号处理和应用处理模块。 [0008] The vehicle-mounted mobile terminal may include a GPS RF front-end module, signal processing and application processing module.

[0009] 所述自学习功能模块可以包括BP神经网络模型,BP神经网络模型的输入神经元包括车辆速度、加速度、车身偏转角速度、方向盘转角变化率和油门或刹车踏板角速度的值,它们作为神经网络系统的输入神经元,经过隐含层的激励函数的作用,得到驾驶员激进、普通、保守其中某一类属的特定类型。 [0009] The module may include a self-learning neural network model BP, BP neural network input neurons model includes a vehicle speed, acceleration, vehicle yaw angular velocity, and steering angle change rate value of the accelerator or brake pedal angular velocity thereof as neuroprotective input neurons network system, through the role of the hidden layer activation function, to give a driver radical, a particular type of normal, wherein a generic conserved.

[0010] 本车辆防撞预警系统可以根据特定类型的历史数据,采用驾驶员释放加速踏板时刻的频数百分位为5 %对应的碰撞时间 [0010] The present vehicle collision avoidance warning system may be based on historical data of a particular type, using the driver releases the accelerator pedal frequency time division hundreds of bits collision time corresponding to 5%

TTCA、启动刹车踏板时刻的频数百分位为5%对应的碰撞时间TTCb和反应时间'3值作 TTCA, start time of the brake pedal frequency division hundreds of bits TTCb impact time and the reaction time corresponding to 5% of '3 values ​​for

为各聚类的特征参数,在防撞分级预警装置动作之后,实时改变阈值计算式的结果,达到实时关注驾驶员行为特性的目的。 Each cluster is characterized by the parameter, after the collision warning device classification operation, changing in real time the result of the threshold value calculation formula, the purpose of real-time characteristics of the driver's behavior of interest.

[0011] 所述自学习功能模块需要两个步骤,首先完成BP神经网络驾驶员类型判断模块, 然后获取驾驶员释放加速踏板时刻的统计聚类百分频数为5%对应的碰撞时间TTCA、启 [0011] The self-learning module requires two steps, the first driver to complete BP neural network type determination module, and then acquires the driver releases the accelerator pedal statistical clustering time percentage corresponding to the frequency of 5% of the time to collision TTCA, start

动刹车踏板时刻的统计聚类百分频数为5%对应的碰撞时间TTCb、驾驶员反应时间、《。 Statistical clustering movable brake pedal frequency time percentage corresponding to 5% of the time to collision TTCB, driver reaction time. "

[0012] TTCa求法是:在车辆处于跟驰状态时,设车距力Ji前后两车的相对速度为Δν [0012] TTCa Seeking: relative speed when the vehicle is in a state with relaxation, Ji force provided from the car before and after the two vehicles is Δν

,则碰撞时间〃C=G。 , The collision time 〃C = G. 经实验研究证明,■体现了驾驶员对追尾危险的判断机制,与潜 The experimental studies have shown, ■ reflects the judgment of the driver's rear-end mechanism dangerous, with the potential

在追尾危险的紧急程度相关;根据驾驶员跟车数据序列,提取出激进、普通、保守三种类型驾驶员释放加速踏板时刻累积ITU的频数,并取每种类型对应频数5%的值为该类型的TTCA ;TTCb求法与TTCa相同。 In the rear risk related to the degree of urgency; sequence according to the driver with the car data extracted radical, general, three types of conserved driver releases the accelerator pedal in time the cumulative frequency of the ITU, and taking the corresponding frequency of each type of value of the 5% type TTCA; TTCb TTCa method for finding the same.

[0013] 所述车辆碰撞判断模块可以采用车辆之间通过DSRC车车通信技术实现本车SV和周围车辆NV交换车辆状态信息,根据车辆的相对位置和相对运动趋势对可能发生的轨迹碰撞划分为纵向防撞、侧向防撞两大类,每类适用不同的阈值计算方法;防撞预警信号分为三级,分别为一级提醒、二级警告和三级报警。 [0013] The vehicle collision determination module implemented SV vehicle around the vehicle and vehicle state information exchange NV-vehicle communication by DSRC technology may be employed between the vehicle collision may occur on the track and the relative position of the vehicle relative movement trend is divided into longitudinal collision, side collision two categories, apply a different threshold for each type of calculation method; collision warning signal is divided into three, namely, a reminder, two and three warning alarms.

[0014] 所述防撞预警装置模块在汽车中控台安装一个显示屏,用示意图标注本车SV与可能碰撞车辆的相对位置,且安装能显示绿、黄、红三种颜色的信号指示灯,显示屏旁设置蜂鸣报警器;预警信号划分为三个级别,级别越高,情况越紧急,其中一级提醒方式为绿灯闪烁,二级警告方式为黄灯闪烁,三级报警方式为红灯闪烁外加蜂鸣报警声。 [0014] The automotive collision warning device module in a console mounted display, schematically denoted by SV relative position of the vehicle may collide with the vehicle, and is mounted to show green, yellow, and red signal lamp , buzzer alarm is provided next to the display; warning signal is divided into three levels, the higher the level, the more an emergency situation, where a prompt mode is blinking, two warning mode is blinking, the alarm mode three red blinking plus beep sounds.

[0015] 本发明提供的上述车辆防撞预警系统,其在实现车辆防撞分级预警功能中的应用。 The vehicle anti-collision warning system provided by the invention [0015], its application, the vehicle anti-collision warning classification functions in implementation.

[0016] 本发明与现有技术相比具有以下的主要的优点: [0016] The present invention and the prior art has the following main advantages:

1.充分利用车载无线传感器网络的数据通信优势,在信息获取方面加入了车身偏转角、方向盘转角、油门踏板角位移、刹车踏板角位移、转向灯状态等数据,对车辆驾驶员特性和未来轨迹做出了比较合理的估计。 1. full advantage of data communication using the in-vehicle wireless sensor network, the added deflection angle of the vehicle body in terms of access to information, steering wheel angle, angular displacement of the accelerator pedal, the angular displacement of the brake pedal, turn lamp status data, and future trajectories of the vehicle driver characteristics make a reasonable estimate.

[0017] 2.用驾驶员释放加速踏板时刻的频数百分位为5%对应的碰撞时间、启动刹车踏板时刻的频数百分位为5%对应的碰撞时间和反应时间表征不同驾驶员的行为特性差异, 把定性问题定量处理,并避免人为干预影响。 [0017] 2. The accelerator pedal is released by the time the driver bit time to collision frequency division hundreds of 5% corresponding to the starting time of the brake pedal frequency division hundreds of bits collision and reaction times of 5% corresponds to characterize different drivers behavioral characteristic differences, the qualitative and quantitative deal with the problem, and to avoid human intervention impact. 系统初始化时,这些量直接取普通类型驾驶员对应的默认值。 At system initialization, the default value of these quantities directly corresponding normal type driver. 行驶过程中的数据积累不断对这两个量进行修正,通过“自学习”功能不断调整防撞分级预警系统提示、报警阈值以适应驾驶员和车辆的实时状况。 Data accumulation process of moving constantly these two amounts is corrected by "self-learning" function continuously adjust the rating collision warning prompted alarm thresholds to accommodate the real-time condition of the driver and the vehicle.

[0018] 3.根据SV和NV的相对位置和相对运动趋势,分为纵向防撞和侧向防撞两大类,分别提供适合的预警算法,在保证安全的前提下提高通行效率。 [0018] The relative positions of the SV and NV relative movement trends into longitudinal and lateral crash collision of two categories, namely, provide a suitable warning algorithm, improve traffic efficiency under the premise of ensuring safety.

[0019] 4.可实现车辆周边360度范围内的防撞预警,消除“盲角”,全面提高行车安全性。 [0019] 4. The collision warning may be implemented outside the range of 360 degrees of the vehicle, eliminating the "blind angle", improve traffic safety.

[0020] 总之,本发明利用车载传感网络实时交换本车SV和周围车辆NV的信息,能对车辆运行状态进行预测、且具备对驾驶员行为特性进行自学习功能的防撞预警系统,提高防撞预警系统的有效性和适应性。 [0020] In summary, the present invention utilizes the vehicle-mounted sensor network real-time exchange of information around the vehicle and the vehicle SV NV, and can predict the vehicle operating state, and the collision warning system includes a driver's behavior characteristics of self-learning, improving effectiveness and adaptability collision warning system.

附图说明 BRIEF DESCRIPTION

[0021] 图1为本发明实例的防撞预警装置整体结构示意图。 [0021] Fig 1 a schematic view of the entire configuration example of collision warning apparatus of the present invention.

[0022] 图2为信息采集处理模块的主要组成部分示意图。 [0022] FIG. 2 is a schematic view of the main part of the information acquisition and processing module.

[0023] 图3为驾驶员的碰撞时间TTC和反应时间'计算流程。 [0023] FIG. 3 is a time to collision TTC and the reaction time of the driver 'calculation process. 其中,TTC (Time-to-co 11 ision)包括驾驶员释放加速踏板时刻的统计聚类百分频数为5 %对应的碰撞时间TTCa*启动刹车踏板时刻的统计聚类百分频数为5%对应的碰撞时间TTCB。 Wherein, TTC (Time-to-co 11 ision) comprises a statistical clustering driver releases the accelerator pedal frequency of the time percentages corresponding to 5% of statistical clustering TTCa * collision time of the brake pedal to start timing the frequency is 5 percent % corresponding to the collision time TTCB.

[0024] 图4为防撞预警装置的结构示意图。 [0024] FIG. 4 is a schematic structural diagram of anti-collision warning device.

[0025] 图中:1.本车SV信息模块;2.自学习功能模块;3.信息存储器模块;4.车辆碰撞判断模块;5.防撞预警装置模块;6. DSRC车车通信模块;7.周围NV车辆防撞预警系统;8. ABS车辆速度获取;9. DGPS信息采集处理;10.其他传感器信息采集处理; 11. BP神经网络模型;12.输入神经元;13.激励函数;14.特定类型;15.历史数据;16.特征参数;17.显示屏;18.蜂鸣报警器;19.北方;20.西方;21.东方;22.南方。 [0025] FIG: a vehicle information module SV; self-learning function module 2; 3 information storage module; 4 vehicle collision block; anti-collision warning device module 5; 6 DSRC vehicular communication module;..... 7. A vehicle anti-collision warning system around NV;. 8 ABS vehicle speed obtaining;. 9 DGPS information collection processing; 10 additional sensor information acquisition process; 11. BP neural network model; 12 input neurons; 13 excitation function; 14. The particular type;. history data 15; 16 characteristic parameters; 17 a display screen;. buzzer alarm 18; 19 North; 20 West; 21 East;.. 22 South.

具体实施方式 Detailed ways

[0026] 本发明提供的基于车载传感网络的车辆防撞预警系统,设有若干DGPS基站,车载GPS接收机终端根据差分基站信号对定位结果进行修正,使车辆定位精度达到Im以内。 [0026] The present invention provides a vehicle anti-collision warning system based on vehicle sensor network, the base station has a plurality of DGPS, GPS receiver terminal location result to the base station based on the differential correction signal to the vehicle location accuracy within Im. 车载端还安装有获取速度、加速度、车身偏转角、方向盘转角、油门或刹车踏板角位移等变量的传感器,以及DSRC车车通信模块综合处理CPU (Central Processing Unit,中央处理器)模块等。 Also mounted on the car side speed acquisition, an acceleration sensor, yaw angle of the vehicle body, the steering wheel angle, brake pedal or the accelerator variable angular displacement, and the DSRC-vehicle communication module integration processing CPU (Central Processing Unit, central processing) module or the like. 油门或刹车踏板角位移传感器的检测结果还可用于估算车辆加/减速度;通过DSRC车车通信实时与周围车辆交换车辆ID、尺寸、位置、运动方向、速度、加速度、车身偏转角、车身偏转角速度、估算加/减速度、转向灯状态、时间等信息;由各传感器采集计算的车辆速度、加速度、方向盘转角变化率、车身偏转角速度、油门或刹车踏板角速度等参数,代入已经经过“学习”处理的BP神经网络模型确定当前驾驶员的类型,并根据驾驶员的实时驾驶操作数据调整防撞预警阈值,实现自学习功能。 A detection result of the accelerator or brake pedal angular displacement sensor may also be used to estimate the acceleration / deceleration of the vehicle; vehicle ID, the size, position, movement direction, speed, acceleration, yaw angle of the vehicle body, real-time exchange with the surrounding body deflection vehicle-vehicle communication by DSRC angular velocity, the estimated acceleration / deceleration, turn signal status, time and other information; a vehicle speed from each sensor to collect the calculated acceleration, steering angle change rate, body yaw angular velocity, the accelerator and brake pedal angular velocity parameters, substituting been subjected to "learn" BP neural network model to determine the type of the current process of the driver, and adjust the anti-collision warning thresholds based on real-time driving operation data of the driver, to achieve self-learning function.

[0027] 所述DSRC车车通信模块综合处理CPU的基本特征为:用于完成DGPS移动端的射频前端模块、信号处理模块、应用处理模块的相关运算;完成传感器的软件滤波、A/D处理及数据计算功能;实现驾驶员分类算法、驾驶行为统计碰撞判断算法处理;完成存储器的数据存取、DSRC设备的信息收发等控制功能。 [0027] The basic characteristics of the communication module integrated-vehicle DSRC processing as a CPU: a mobile terminal for performing DGPS RF front-end module, a signal processing module, the correlation operation of the application processing module; filtering software to complete the sensor, A / D processing and data calculation; classification algorithm implemented driver, driving behavior statistics collision determination algorithm processing; complete memory data access, messaging device DSRC control functions.

[0028] 下面结合实施例及附图对本发明作进一步说明,但并不局限于下面所述内容。 [0028] The following examples and in conjunction with the accompanying drawings of the present invention is further illustrated, but not limited to the following contents.

[0029] 本发明提供的基于车载传感网络的车辆防撞预警系统,其结构如图1所示,包括本车SV信息模块1、自学习功能模块2、信息存储器模块3、车辆碰撞判断模块4、防撞预警装置模块5、DSRC车车通信模块6和周围NV车辆防撞预警系统7,其中:本车SV信息模块1由信息采集处理模块和包括车辆本身的ID及尺寸固定信息的模块组成,信息采集处理模块经自学习功能模块2、车辆碰撞判断模块4与防撞预警装置模块5进行数据交换,车辆本身的ID及尺寸固定信息的模块经信息存储器模块3、车辆碰撞判断模块4与防撞预警装置模块5进行数据交换。 [0029] The present invention provides a vehicle anti-collision warning system based on vehicle sensor network, which is shown in Figure 1, comprising a vehicle information module SV 1, 2 self-learning function modules, information memory module 3, a vehicle collision block 4, collision warning device module 5, DSRC vehicular communication module 6 and the surrounding vehicle collision warning systems NV 7, wherein: SV vehicle information collection module 1 by the information processing module including the vehicle itself, and ID information and the size of the fixed modules composition, the information acquisition and processing module is self-learning module 2, module 4 determines a vehicle collision with the collision warning data exchange device module 5, the information module is a memory module of the vehicle itself and the size of the fixed ID information 3, a vehicle collision determination module 4 exchange data with the anti-collision warning device module 5. 信息存储器模块3经DSRC车车通信模块6与周围车辆NV防撞预警系统7实现DSRC无线通信。 Information memory module 36 and around the vehicle collision warning systems NV realized via DSRC radio communication module 7 DSRC-vehicle communication.

[0030] 所述信息采集处理模块的主要内容见图2。 [0030] The primary content acquisition information processing module shown in Figure 2. 该模块需要采集的信息分为ABS车辆速度获取8、DGPS信息采集处理9和其他传感器信息采集处理10三大类,其中:DGPS信息采集处理9包括卫星、差分式GPS基站及车载端移动GPS模块,处理后得到的信息有车辆位置和绝对运动方向;车载端移动GPS模块包括射频前端、信号处理和应用处理模块。 The information to be collected into module ABS vehicle speed obtaining 8, DGPS information collection processing other sensors 9 and 10 three categories of information collection processing, wherein: DGPS information collection processing including satellite 9, Differential GPS base station and a mobile GPS car end module , processing information are obtained and the absolute position of the moving direction of the vehicle; mobile GPS vehicle terminal comprises a RF front-end module, signal processing and application processing module. 因现在不少车辆配置有ABS防抱死制动系统,该系统本身具有非常精确的车速检测结果,故ABS车辆速度获取不再额外加传感器,可对该系统简单改造,在ABS控制器上外加一个数据输出的对应接口,进行防抱死算法运算的同时向外传送精确的车辆速度数值以备用。 Because many vehicles are now disposed ABS braking system, the system itself has a very precise detection result of the vehicle speed, vehicle speed obtaining ABS so no extra sensors, plus the ABS controller in the system simple transformation a data output corresponding to an interface, an arithmetic operation simultaneously transmitted outwardly antilock accurate vehicle speed value to spare. 其他传感器信息采集处理10包括双轴加速度传感器、车身偏转角(陀螺仪)传感器、光电感应式方向盘转角传感器、油门和刹车踏板角位移传感器,因为其车辆加速度、车身偏转角、方向盘转角、 油门或刹车踏板角位移这些值的获取过程基本相似,都是先用放大电路放大,然后输入CPU 进行软件滤波和A/D转换的处理。 Other sensor information acquisition process 10 includes a biaxial acceleration sensor, a yaw angle of the vehicle body (gyro) sensor, a steering wheel angle sensor type photoelectric sensors, throttle and brake pedal angular displacement sensor, because it is the vehicle acceleration, vehicle yaw angle, steering angle, throttle or the angular displacement of the brake pedal acquisition procedure substantially similar values, are amplified by the first amplifying circuit, and then input to a CPU for processing software filtering and a / D conversion. 车身偏转角、方向盘转角和踏板角位移对时间分别进行微分计算,还可以得到车身偏转角速度、方向盘转角变化率和油门或刹车踏板角速度;为了实现对车辆状态的预测,根据车辆机械动力转换原理,可代入油门或刹车踏板角位移,得到下一时刻车辆的估算加速度。 Body deflection angle, steering wheel angle and the pedal angular displacement of the time differential calculation, respectively, can also get the vehicle body yaw angular velocity, the steering angle change rate accelerator or brake pedal angular velocity; order to predict the vehicle state, the vehicle mechanical power conversion principle, may be substituted into the accelerator or brake pedal angular displacement to obtain an estimated acceleration of the vehicle next time.

[0031] 上述DGPS模块的基本特征为:差分基站采用伪距差分式设备,按一定范围分布, 尽可能减少基站和移动式GPS的距离所带来的系统误差。 [0031] The basic features of the above DGPS module is: a base station using a differential pseudorange differential devices, according to a certain distribution range, and to minimize the distance of the mobile station caused by the GPS system errors. 为保证数据获取的实时性、准确性,这里所采用的差分式GPS的频率至少为5HZ以上,定位误差在Im之内。 In order to ensure real-time, accurate data acquisition, Differential GPS frequency employed herein is at least 5HZ above, the positioning error within Im. 该模块具有数据I/O端口供外界读取其输出数据。 The module has a data I / O port for external read data output.

[0032] 上述双轴加速度传感器的基本特征为:该装置平放且固定在汽车驾驶室内的适当部位。 [0032] The basic characteristics of the biaxial acceleration sensor is: the apparatus is fixed in place and flat car cab portion. 核心部分的双轴加速度传感器的X轴、Y轴输出信号送入放大电路放大,经软件滤波、 A/D转换逐级处理之后,通过对应接口向外输出较精确的车辆加速度信息。 Biaxial X-axis acceleration sensor core section, Y-axis output signal of the amplification circuit is fed, step by step after the filtering process by software, A / D conversion, the acceleration information outputted by the corresponding interface outwardly more accurate vehicle.

[0033] 上述车身偏转角(陀螺仪)的传感器的基本特征为:所采用的传感器为陀螺仪,输出信号送入放大电路放大,经软件滤波、A/D转换逐级处理之后,通过对应接口向外输出较精确的车身偏转角信息。 [0033] The basic characteristics of the deflection angle (gyro) sensor to the vehicle body: a sensor used as a gyroscope, the output signal into the amplification circuit, the software filter, A / D conversion process after stepwise through the corresponding interface output of the vehicle body outward deflection angle more precise information.

[0034] 上述光电感应式方向盘转角传感器的基本特征为:采用光电感应式方向盘转角传感器,既判断方向盘的转向,同时输出两个时刻之间的相对转角。 [0034] The basic characteristics of the photoelectric sensor to steering wheel angle sensor: photoelectric inductive steering wheel angle sensor, the steering wheel is determined only, while relative rotation between the two output time. 把传感器输出信号送入放大电路放大,经软件滤波、A/D转换逐级处理之后,通过对应接口向外输出较为精确的方向盘转角信息。 The sensor output signal amplification circuit is fed, after filtering by software, A / D conversion processing step by step, more precise steering angle output information outwardly through the corresponding interface. 进一步的,把该信息进行微分处理,可得到方向盘转角的变化率。 Further, this information is differentiated, the steering angle change rate is obtained.

[0035] 上述油门和刹车踏板角位移传感器的基本特征为:分别给油门踏板和刹车踏板安装角位移传感器,送入放大电路放大,经软件滤波、A/D转换逐级处理之后,得到较为精确的角位移信息,通过对应接口往外送出。 [0035] The above-described basic characteristics of the accelerator and brake pedal angular displacement sensor as: respectively, to an accelerator pedal and a brake pedal mounted angular displacement sensor, into the amplification circuit, the software filter, after A / D conversion processing step by step, to obtain more accurate the angular displacement information sent out via the corresponding interface. 由于油门踏板和刹车踏板不可能同时踩下,故只输出一路角位移信号,再控制另一I/O 口的高低电平便于下级输入判断此信号来自油门踏板还是刹车踏板。 Since the accelerator pedal and depresses the brake pedal can not simultaneously, so the only way the output of the angular displacement signal, and then to control another I / O port of the high or low level to facilitate this determination lower input signal from the accelerator pedal or brake pedal. 根据车辆机械动力转换原理,可由此角位移算出下一时刻车辆的估算加速度。 The vehicle mechanical power conversion principle, whereby the angular displacement may be calculated next time the vehicle acceleration estimated.

[0036] 所述自学习功能模块2用于根据驾驶员操作行为数据,判断驾驶员的类型,并调整防撞预警阈值。 [0036] The self-learning module 2 according to the operation behavior of the driver for the data, determine the type of driver, and adjusting the alarm threshold collision. 其结构如图3所示,包括BP神经网络模型11、输入神经元12,其中:BP 神经网络模型主要是判断驾驶员的类型。 The structure shown in Figure 3, comprises a neural network model BP 11, input neurons 12, wherein: BP neural network model primarily determine the type of driver. 输入神经元包括车辆速度、加速度、车身偏转角速度、方向盘转角变化率和油门或刹车踏板角速度等的值巧0 = 1,2Λ4,5),它们作为神经网络 Input neurons comprising a value of the vehicle speed, acceleration, vehicle yaw angular velocity, steering wheel angle and rate of change of the accelerator or brake pedal angular velocity clever 0 = 1,2Λ4,5), which as a neural network

系统的输入神经元,经过隐含层的激励函数13的作用,得到驾驶员激进、普通、保守其中某一类属的特定类型14。 Input neurons system, acting through the hidden layer activation function 13 to obtain a driver radical, a particular type of common, generic one where Conservative 14. 根据特定类型14的历史数据15,选择驾驶员释放加速踏板时刻的 15, the driver releases the accelerator pedal to select the time based on historical data of a particular type 14

频数百分位为5%对应的碰撞时间TTCa、启动刹车踏板时刻的频数百分位为5%对应的碰 Frequency division hundreds of bits collision time corresponding to 5% TTCa, start time of the brake pedal frequency division hundreds of bits corresponding to the touch of 5%

撞时间TTCb和反应时间'5值作为各聚类的特征参数16,在防撞分级预警装置动作之后, TTCb collision time and the reaction time '5 each cluster value as the characteristic parameters 16, after the collision warning classification operation means,

实时改变阈值计算式的结果,达到实时关注驾驶员行为特性的目的。 Real-time calculation formula to change the result of the threshold, to achieve the purpose of real-time behavioral characteristics of the driver's attention.

[0037] 所述自学习功能模块2需要两个步骤,首先完成BP神经网络驾驶员类型判断模 [0037] The self-learning module 2 requires two steps, first complete BP neural network driver type judgment module

块,然后获取驾驶员释放加速踏板时刻的统计聚类百分频数为5%对应的碰撞时间TTCa、 启动刹车踏板时刻的统计聚类百分频数为5%对应的碰撞时间TTCb、驾驶员反应时间'- Block, then acquires the time the driver releases the accelerator pedal statistical clustering percentages frequency of collision time corresponding to 5% TTCA, statistical clustering start time of the brake pedal percentage frequency of collision time corresponding to 5% of TTCB, driver Reaction time'-

ο ο

[0038] TTCa求法是:在车辆处于跟驰状态时,设车距力I前后两车的相对速度为Δν ,则碰撞时间。 [0038] TTCa seeking method is: when the vehicle is in a state with relaxation, the force provided from the vehicle longitudinal relative velocity between the vehicle I is Δν, the impact time. 经实验研究证明,体现了驾驶员对追尾危险的判断机制,与潜在追尾危险的紧急程度相关;根据驾驶员跟车数据序列,提取出激进、普通、保守三种类型驾驶员释放加速踏板时刻累积的频数,并取每种类型对应频数5%的值作为该类型的TTCa。 Experimental studies have shown that the mechanism reflects the judgment of the driver of the rear-end collision danger, the risk associated with the urgency of a potential rear-end; according to the driver with the car sequence data extracted radical, ordinary, a conservative three types of driver releases the accelerator pedal cumulative time of the frequency, and taking the frequency value corresponding to each type of 5% as the type TTCa. TTCb求法和TTCa类似。 TTCb calculation methods and TTCa similar.

[0039] 所述信息存储器模块3也被划分为本车SV信息存储区和周围车辆NV的信息存储区,以便车辆碰撞判断模块和DSRC车车通信模块获取正确的值。 [0039] The information memory module 3 is also divided SV information storing area that the vehicle around the vehicle, and the NV store information, so that the vehicle collision block-vehicle DSRC communication module and obtaining the correct value.

[0040] 所述车辆碰撞判断模块4采用了车辆之间通过DSRC车车通信技术实现本车SV和周围车辆NV交换车辆状态信息,根据车辆的相对位置和相对运动趋势对可能发生的轨迹碰撞划分为纵向防撞、侧向防撞两大类,每类适用不同的阈值计算方法。 [0040] The vehicle collision determination module 4 uses vehicle status information exchange between the vehicle and achieve the vehicle around the vehicle SV NV-vehicle communication by DSRC technology, a collision may occur on the track is divided according to the relative position of the vehicle relative movement trends is a longitudinal collision, side collision two categories, different threshold value calculation method is applicable to each category. 防撞预警信号分为三级,分别为一级提醒、二级警告和三级报警。 Collision warning signal is divided into three, namely, a reminder, two and three warning alarms. 本车SV信息模块包含传感器实时采集信息和车辆固有属性信息。 SV vehicle information collection module includes a sensor in real time and the vehicle information unique attribute information.

[0041] 所述防撞预警装置模块5的结构如图4所示:在车辆中控台安装一个显示屏17,用示意图标注可能碰撞的NV车辆与本车SV的相对位置,且安装能显示绿、黄、红三种颜色的信号指示灯,供驾驶员查看靠近的周围车辆NV的信息存储区(简称NV)和本车SV信息存储区(简称SV)的相对位置,对不同方向(北方19、西方20、东方21、南方22)的一级提醒、二级警告分别亮起绿灯和黄灯表示,三级报警信号产生时则红灯闪烁,预警信号划分为三个级别,级别越高,情况越危险。 Structure [0041] The anti-collision warning device module 5 is shown in Figure 4: a screen mounted in the center console of the vehicle 17, the relative position of the labeled schematically a possible collision with the vehicle NV SV of the vehicle, and is mounted to show the relative position of green, yellow, and red signal lamp for the driver to view the surroundings of the vehicle near the NV store information (referred to as NV) and vehicle information storage area SV (SV for short), and in different directions (north 19, 20 West, 21 East, South 22) of an alert, warning, respectively, two green light and yellow light indicates, the three red light flashes when the alarm signal is generated, warning signal is divided into three levels, the higher level , the situation is more dangerous. 一级提醒方式为绿灯闪烁,二级警告方式为黄灯闪烁,三级报警方式为红灯闪烁外加蜂鸣报警声。 A prompt mode is blinking, two warning mode is blinking, the alarm mode of three plus red light flashing alarm buzzer. 此外显示屏旁还安装了一个蜂鸣报警器18,辅助三级报警的声音提示,告诫驾驶员立即采取行动防止可能的碰撞。 Also next to the display also installed an alarm buzzer 18, an auxiliary stage alarm, audible warn the driver to take immediate action to prevent a possible collision.

[0042] 所述DSRC车车通信模块6的基本特征为:针对点对多的车车通信方式特点,采用5.8GHZ频段,更新频率至少为10ΗΖ,全双工通信方式。 [0042] The DSRC vehicular communication module 6 of the basic characteristics: the characteristics of point to multipoint communication system truck using 5.8GHZ band, the update frequency of at least 10ΗΖ, full duplex communication. 与周围车辆NV交换的信息帧为:帧头、车辆ID、尺寸、位置、运行方向、速度、加速度、车身偏转角、车身偏转角速度、转向灯状态、估算加速度、同步时间、帧尾。 NV vehicle information exchange with the surrounding frame is: Preamble, vehicle ID, the size, position, running direction, velocity, acceleration, vehicle yaw angle, yaw angular velocity of the vehicle body, turn signal status, acceleration estimation, time synchronization, end of frame.

[0043] 所述转向灯状态的测量方法为:可由内部电路读取左转向灯或者右转向灯的开关状态得到。 [0043] The turn lamp state measurement method is as follows: by the internal circuit reads right turn or the left turn signal lamp switch state is obtained. 若两者均未开,则赋予转向灯状态的变量为0 ;若左转向灯开,则该变量置为1 ;若右转向灯开,变量值赋成2。 If neither is on, the turn signal assigned to the variable state is 0; if the opening left turn signal, the variable is set to 1; if the right turn lights on, the value assigned to the variable 2.

[0044] 本发明提供的上述基于车载传感网络的车辆防撞预警系统,能够实现车辆防撞分级预警功能,具体是:实时采集的信息包括车载DGPS模块采集的车辆位置和运动方向信息、从车辆ABS控制器获取的车辆速度信息、双轴传感器检测的车辆加速度信息、陀螺仪检测的车身偏转角及微分运算所得的车身偏转角速度信息、光电感应式方向盘转角传感器检测的方向盘转角及由此得到的方向盘转角变化率和估算加速度信息、油门或刹车踏板角位移传感器检测的油门或刹车踏板角位移及由此测算得的油门或刹车踏板角速度和估算加速度信息、转向灯开关状态信息,以及由自学习功能模块判断得出的驾驶员类型信息。 [0044] The present invention provides a vehicle anti-collision warning system based on vehicle sensor network can be realized vehicle collision warning classification function, in particular above: real-time collection of information including vehicle position and direction of movement of the vehicle-mounted DGPS module collects information from vehicle ABS controller acquires vehicle speed information, the biaxial acceleration sensor for detecting vehicle information, a vehicle body yaw angle detected by the gyro and calculating the vehicle body obtained by differentiating the yaw angular velocity information, the steering wheel angle sensor type photoelectric sensor for detecting the steering wheel angle and whereby steering wheel angle and rate of change of acceleration information, the accelerator and brake pedal sensor for detecting the angular displacement of the angular displacement of the accelerator or brake pedal or the accelerator and brake pedal angular velocity estimate and the estimated acceleration information calculation thus obtained, blinker switch state information, and by the self- learning function module determines the type of information the driver obtained. 车辆固有属性信息包括车辆ID、车辆尺寸等。 Vehicle unique attribute information includes vehicle ID, the vehicle size and the like.

[0045] 本发明提供的上述基于车载传感网络的车辆防撞预警系统,其突出效果是:该系统利用车载传感网络,实时交换车辆之间的当前状态信息,并对其行驶状态进行预测,同时,通过本车实时状态信息对驾驶员行为特性进行自学习来预测驾驶员的性格特点,进而判断本车与周围车辆之间是否会发生碰撞。 [0045] The present invention provides a collision warning system of the vehicle based on vehicle sensor network, which protrudes effect: The system uses vehicle sensor network, real-time exchange of information between the current state of the vehicle, and predict the travel state at the same time, self-learning to predict personality traits driver with real-time status information on the behavior characteristics of the driver of the vehicle, and then determine whether a collision will occur between the vehicle and the surrounding vehicles. 当可能发生碰撞事故时,向驾驶员发出预警提示,便于驾驶员及时采取措施避免碰撞事故,从而提高道路交通安全水平。 When a collision accident occurred, issued a warning to prompt driver, to facilitate the driver to take timely measures to avoid a collision, thus improving road safety.

[0046] 本发明提供的上述基于车载传感网络的车辆防撞预警系统,在实现车辆防撞分级预警功能应用时,其步骤包括: [0046] The vehicle collision avoidance warning system of the present invention provides a vehicle-based sensor networks, when implementing the vehicle collision warning classification application function, comprising the steps of:

步骤1 :汽车点火启动时,汽车车上原有的车载差分式GPS接收装置、传感器采集模块、DSRC车车无线通信设备等完成启动及初始化设置。 Step 1: start the car ignition, the original automotive vehicle car Differential GPS receiving device, a sensor acquisition module, the DSRC-vehicle wireless communication device to complete start-up and initial setting and the like. 初始化时,驾驶员类型默认为普通型,其 During initialization, the driver type defaults to Normal, which

释放加速踏板时刻的频数百分位为5%对应的碰撞时间TTCa、启动刹车踏板时刻的频数 Accelerator pedal is released hundreds of time frequency division bit collision time corresponding to 5% TTCa, the frequency of the brake pedal to start the time

百分位为5%对应的碰撞时间TTCb和驾驶员反应时间Irei均选择普通型的对应值。 5% percentile collision time corresponding to the driver's reaction time and TTCb Irei general type are selected corresponding value.

[0047] 步骤2 :把DGPS的车载终端的天线接收信号导入汽车车上原有的射频前端模块、信号处理模块、应用处理模块,在逐级处理之后,可得到本车SV的二维绝对位置坐标P(x,y)及绝对运动方向w。 [0047] Step 2: The vehicle terminal DGPS antenna reception signals introduced automotive vehicle original RF front-end module, a signal processing module, application processing module, after the process step by step, the absolute two-dimensional position coordinates of the obtained vehicle SV P (x, y) and the absolute direction of movement w.

[0048] 然后,把车辆ID、尺寸、本车SV的位置坐标P(Ay)、运行方向速度ν、加速度a [0048] Then, the vehicle ID, the size, the position coordinates of the vehicle SV P (Ay), the running direction of velocity v, acceleration a

、车身偏转角θ、车身偏转角速度Δ5、估算加速度iV,、转向灯状态FLAG及当前同步时间等 , The deflection angle [theta] of the vehicle body, a vehicle body yaw angular velocity [Delta] 5, the estimated acceleration iV ,, turn signal FLAG and the current synchronization state time

状态值作为一个信息帧,通过DSRC车-车通信方式,与周围车辆NV实时交换,并把发出去的和接收到的数据存储起来。 Status value as an information frame, by the DSRC vehicle - vehicle communication mode, the real-time exchange NV around the vehicle, and to store the sent and received data together. 这里的周围车辆NV是指距本车SV —定范围内的一辆或多辆车,但对周围车辆NV进行排序处理,每个方位重点关注与本车SV距离最近的一辆。 NV vehicles around here is the distance the vehicle SV - one or more vehicles within a given range, but around the vehicle NV sorting process, each orientation with a focus on the recent SV from the vehicle.

[0049] 根据车辆尺寸和位置坐标信息,定义SV和NV车辆实体部分最接近的点为两者的 [0049] The vehicle position coordinate and size information, definitions and SV NV vehicle closest point to a substantial portion of both the

距离,即ά = ^1-X2)2Hy1-y2f,其中和(〜Λ)为两车车体部分最近的两个坐标 Distance, i.e. ά = ^ 1-X2) 2Hy1-y2f, and wherein (~Λ) for the latest two car body part two coordinates

点。 point. 若m,则告诉驾驶员100米范围之内有车辆行驶,防撞分级预警装置给出绿灯亮的一级提示。 If m, told with vehicles within 100 meters of the driver, the collision warning classification means given a green light tips. 接下来,如何判断二级警告或三级报警将重点进行叙述,二级警告是提醒驾驶员必须提高注意力,三级报警则要求驾驶员必须对当前情况作出加速或减速或转弯的调離 Next, how to determine two or three warning alarm will focus on narrative, two warning is to alert the driver must improve concentration, three alarm requires the driver must make acceleration or deceleration or turning transferred to the current situation

IF. ο IF. Ο

[0050] 根据SV和NV的相对位置,分别转入纵向防撞、侧向防撞的对应类别: [0050] The relative position of the NV and SV, respectively, into the collision longitudinal, lateral crash corresponding categories:

1.若SV和NV处于同一条路上,无转入旁路分支的意向,车身边缘沿其运动方向有重叠,且方向角一致,即满足下述公式时,转入纵向防撞的预警规则,该公式为: 1. If the SV and NV is the same road, no intention into the bypass branch, the body edge overlap along its direction of movement, and the angle in the same direction, i.e., satisfy the following equation, into the longitudinal collision warning rules, the formula is:

lv2-y1\>L1 lv2-y1 \> L1

I^-J - itj J ^—w I ^ -J - itj J ^ -w

FLAG = O..«1 = «2 FLAG = O .. «1 =« 2

式中M力本车SV的车身宽度。 M in formula body width SV force of the vehicle. “力SV的车身长度。(X1J1)和(¾,¾)为两车车体部分最近的两个坐标点。FLAG为转向灯状态,其中,FLAG=O,直行;FLAG=I,左转;FLAG=2,右转。巧禾口为 。 . "Body length of the force SV (X1J1) and (¾, ¾) of two car body section nearest to two coordinate points .FLAG turn signal state, wherein, FLAG = O, straight; FLAG = I, left; FLAG = 2, turn right. He Qiao mouth is.

[0051] 预测时间Tpre之后,本车SV和周围车辆NV的间距预测结果是: [0051] After prediction time Tpre, the pitch predictor and the vehicle around the vehicle SV NV is:

= (¾ - VlF声+1 ia2We —〜rJ1^ + d , = (¾ - VlF acoustic +1 ia2We -~rJ1 ^ + d,

式中:Tpn为预测周期,通常取3秒;alwe、〜e分别为本车SV和周围车辆NV的加速度预测值。 Where: Tpn prediction period, usually take 3 seconds; acceleration prediction value SV vehicles around the vehicle and the NV alwe, ~e were present. [0052] 二级警告需满足的条件是:ώ辨S TTCa *|(v2 +〜〜-(V1 + A (1)(D0是设定的最小安全距离),此时预警装置亮起黄灯提示驾驶员。 Conditions [0052] The two warnings are to be fulfilled: ώ Resolution S TTCa * | (v2 + ~~- (V1 + A (1) (D0 is the minimum safety distance is set), then warning means is yellow prompt the driver.

[0053] 三级报警需满足的条件是^ SnU^b+Ay-h+hJ^I + Z)。 Conditions [0053] The three alarm is to be met ^ SnU ^ b + Ay-h + hJ ^ I + Z). (2)此时预警装置红灯闪烁,且报警器动作,提示驾驶员采取相应操作避免碰撞事故发生。 (2) At this time the red light flashing warning device, and the operation of the alarm, the driver is prompted to take appropriate action to avoid a collision accident.

[0054] 以上是跟驰情况下的预警,若运动方向相对(即啊=-¾ ),则需对上述预警规则作简单变换,化为纵向相对碰撞的预警。 [0054] The above is the case with the warning override condition, when the direction of relative movement (i.e., ah = -¾), the above-described need for a simple alarm rule conversion into collision warning longitudinally opposite. 此时对两车的预期距离进行加法运算,即二级警告需满足的条件是^je ^τιυΑή(ν2+O2pJ+Cn+νΛ«)|+Ζ)ο, (3)三级报警需满足的条件是 At this time, the expected distance between the two vehicles will be adding, i.e. conditions to be fulfilled is that two warning ^ je ^ τιυΑή (ν2 + O2pJ + Cn + νΛ «) | + Ζ) ο, (3) to be met three alarm requirement is

' <·πυΒ +¾^)+Cv1+Vir^)|+D0。 '<· ΠυΒ + ¾ ^) + Cv1 + Vir ^) | + D0.

2.若SV和NV的运动方向满足Kl,则转入侧向防撞流程。 2. If the direction of motion of the SV and NV satisfy Kl, then the process proceeds to a lateral collision. 经过时间ί之后,SV的位置、NV的位置分别变为(叉、(〜『y2fJ ,且(hfr^yip-J = (¾ +t ■ (Ά +ί. αι) ■ +ί. AfflLy1 +£ ■ O1 +ί ·<). 3ΐη(^ + £·Δφ) 、 After time ί, SV position, NV positions were changed (fork, (~ 'y2fJ, and (hfr ^ yip-J = (¾ + t ■ (Ά + ί. Αι) ■ + ί. AfflLy1 + £ ■ O1 + ί · <). 3ΐη (^ + £ · Δφ),

(xSfK'^m) = (χ2 +ί ■ (ν2 +£ ■ α2) · c_2 +t · J2 +ί ■ (V2 +ί ■ (¾) ■ SlU(&2 +£ ■ Α&2))。 若 (XSfK '^ m) = (χ2 + ί ■ (ν2 + £ ■ α2) · c_2 + t · J2 + ί ■ (V2 + ί ■ (¾) ■ SlU (& 2 + £ ■ Α & 2)). If

% = <ί (ί为一极小的正数),则说明未来轨迹有交汇 % = <Ί (ί is a very small positive number), then there is the next intersection the track

点,有发生碰撞的可能性。 Point, there is likelihood of a collision. 当Tptt+'ΐΐ (5)预警装置亮起黄灯;三级报警发生于 When Tptt + '(5) means ΐΐ warning is yellow; three alarm in

(6),红灯闪烁且报警器动作,要求驾驶员采取转向或减速等操作。 (6), and the red light flashing alarm operation, a steering request of the driver or the like to take decelerating operation. 这里的tres tres here

是指频数百分位为5%对应的反应时间驾驶员反应时间,它是各聚类驾驶员的特征参数。 Frequency division means hundreds of bits corresponding to 5% of the reaction time of the driver's reaction time, which is a characteristic of each cluster driver.

[0055] 步骤3 :应用BP神经网络模型11对驾驶员的实时操作数据按照已经经过实验验证并聚类处理得到的驾驶员行为特性归类,实现“自学习”功能,具体方法如下:BP神经网络模型11的输入层1 = {、;^;'々}有5个神经元:^ = 1,2,3,4,5),分别表示车辆 [0055] Step 3: real-time operation data of the mold 11 BP neural network according to the driver and has passed experiments clustering driver behavior characteristics obtained classification process, to achieve "self-learning" function, as follows: BP neural network model input layer 11 is 1 = {,; ^; '々 has five neurons}: ^ = 1,2,3,4,5), respectively, of the vehicle

速度ν、加速度υ、车身偏转角速度Δ5、方向盘转角变化率△识和油门或刹车踏板角速度 Velocity ν, acceleration υ, vehicle yaw angular velocity Δ5, steering wheel angle and rate of change △ identification angular velocity accelerator or brake pedal

;隐层有若干神经元,并且已选择了适合传递函数;网络的3个输出向量Y1 =(1Λ0)、 ; A number of hidden layer neurons, and for the transfer function has been selected; 3 network output vector Y1 = (1Λ0),

Y2 =(0丄0)、Y3=(CUU)分别代表激进、普通、保守三种类型的驾驶员。 Y2 = (0 Shang 0), Y3 = (CUU) represent the radical general, three types of driver conserved. 而且,在BP神经网 Moreover, BP neural network

络模型11正式工作之前,需要完成BP神经网络模型的学习过程:分别对三种典型行为特性的驾驶员采集大量操作数据并送入网络的输入端,同时将相应的期望输出与网络输出相比较,得到误差信号,以此控制权值连接强度的调整,经多次训练后收敛到一个确定的权值;通过对不同的样本情况修改权值,提高该模型的适应性。 11 before formal job network model, complete BP neural network model learning process: the driver are three typical behavioral characteristics collect a large number of operating data and input into the network, while the corresponding desired network output is compared with the output to give an error signal, thereby controlling the strength of the connection weight adjustment, after several trained weights converge to a certain value; adaptability to various modifications by weight of the sample, the increase of the model.

[0056] 把输入神经元12相关的各变量= 1,2,3Α5)送入经过训练的网络后,经过运算 After the [0056] related to the input neurons 12 each variable = 1,2,3Α5) into the trained network, through the operation

得到当前驾驶员的隶属类型,再用针对不同类型驾驶员的算法进行参数匹配。 Get the current driver of the membership type, then the parameters for different types of drivers matching algorithms. 该算法来自于各个聚类的驾驶员的统一统计。 The algorithm from the driver's unified statistics for each cluster. 根据该驾驶员类型选择驾驶员释放加速踏板时刻的频数百分位为5%对应的碰撞时间TTCa、启动刹车踏板时刻的频数百分位为5%对应的碰撞时间TTCb ,代入步骤2的公式(1)、(2)、(3)、(4),可获得与驾驶员实时状态对应的纵向防撞 According to this type of driver to select the time the driver releases the accelerator pedal frequency division hundreds of bits collision time corresponding to 5% TTCa, start frequency division hundreds of bits corresponding to 5% of the time to collision time of the brake pedal TTCb, Step 2, substituting equation (1), (2), (3), (4), the real-time state of the driver can be obtained with a corresponding longitudinal collision

预警阈值;选择频数百分位为5%对应的反应时间'.,值作为各聚类驾驶员的特征参数,代入步骤2的公式(5)和公式(6),对可能发生的侧向防撞进行判断,并给出预警信号。 Warning threshold; choice reaction time frequency division hundreds of bits corresponding to 5% ', the value of each cluster as the characteristic parameter of the driver, into the formula (5) and (6) step 2 may occur on the side. collision judgment, and give early warning signals.

Claims (9)

1. 一种基于车载传感网络的车辆防撞预警系统,其特征是包括本车SV信息模块、自学习功能模块、信息存储器模块、车辆碰撞判断模块、防撞预警装置模块、DSRC车车通信模块和周围NV车辆防撞预警系统,其中:本车SV信息模块由信息采集处理模块和包括车辆本身的ID及尺寸固定信息的模块组成;信息采集处理模块经自学习功能模块、车辆碰撞判断模块与防撞预警装置模块进行数据交换;车辆本身的ID及尺寸固定信息的模块经信息存储器模块、车辆碰撞判断模块与防撞预警装置模块进行数据交换;信息存储器模块经DSRC车车通信模块与周围车辆NV防撞预警系统实现DSRC无线通信。 CLAIMS 1. A vehicle anti-collision warning system based on vehicle sensor network, wherein the vehicle includes a SV information module, self-learning module, memory module information, a vehicle collision block, collision warning device module, the DSRC-vehicle communication NV module and surrounding the vehicle collision warning systems, wherein: the vehicle SV information by the information collection module comprises a processing module and the vehicle itself and the size of the fixed ID information module; information acquisition and processing module is self-learning module, a vehicle collision block collision warning device module with data exchange; information module is a memory module of the vehicle itself and the size of the fixed ID information, the vehicle collision avoidance warning and collision block device module data exchange; the DSRC vehicular communication module information around the memory module is NV vehicle collision warning systems to achieve DSRC radio communication.
2.如权利要求1所述的车辆防撞预警系统,其特征在于所述信息采集处理模块需要采集的信息分为ABS车辆速度获取、DGPS信息采集处理和其他传感器信息采集处理三大类, 其中:DGPS信息采集处理包括卫星、差分式GPS基站及车载端移动GPS模块,处理后得到的信息有车辆位置和绝对运动方向;ABS车辆速度获取不再额外加传感器,直接从ABS控制器中把车辆速度数值输出备用;其他传感器信息采集处理包括双轴加速度传感器、车身偏转角、光电感应式方向盘转角传感器、油门&刹车踏板角位移传感器。 2. The vehicle collision avoidance warning system as claimed in claim 1 DGPS information acquisition and processing and the sensor information acquisition processing other three categories, characterized in that said information acquisition and processing module to be collected into a vehicle speed obtaining ABS, wherein : DGPS information collection processing including satellite, differential GPS base station and a mobile GPS car end module, processing information are obtained and the absolute position of the moving direction of the vehicle; a vehicle speed obtaining ABS no extra sensor, directly from the ABS controller of the vehicle Numerical output of the standby speed; additional sensor information acquisition process includes a biaxial acceleration sensor, a yaw angle of the vehicle body, a steering wheel angle sensor type photoelectric sensor, a brake pedal angle & accelerator displacement sensor.
3.如权利要求2所述的车辆防撞预警系统,其特征在于所述车载端移动GPS模块包括射频前端、信号处理和应用处理模块。 Vehicle collision avoidance warning system according to claim 2, characterized in that the vehicle comprises a side mobile GPS RF front-end module, signal processing and application processing module.
4.如权利要求1所述的车辆防撞预警系统,其特征在于所述自学习功能模块包括BP神经网络模型、输入神经元,其中:BP神经网络模型主要是判断驾驶员的类型;输入神经元包括车辆速度、加速度、车身偏转角速度、方向盘转角变化率和油门或刹车踏板角速度的值, 它们作为神经网络系统的输入神经元,经过隐含层的激励函数的作用,得到驾驶员激进、普通、保守其中某一类属的特定类型。 Input neurons; BP neural network models are primarily determine the type of the driver: 4. The vehicle collision avoidance warning system as claimed in claim 1, wherein said functional module comprises a self-learning neural network model BP, input neurons, wherein element includes a vehicle speed, acceleration, vehicle yaw angular velocity, and steering angle change rate value of the accelerator or brake pedal angular velocity, which is a neural network input neurons, through the effect of the hidden layer activation function, to give a driver radical, Common conservative class type wherein a particular genus.
5.如权利要求4所述的车辆防撞预警系统,其特征在于:根据特定类型的历史数据,采用驾驶员释放加速踏板时刻的频数百分位为5%对应的碰撞时间TTCA、启动刹车踏板时刻的频数百分位为5%对应的碰撞时间TTCb和反应时间'3值作为各聚类的特征参数,在防撞分级预警装置动作之后,实时改变阈值计算式的结果,达到实时关注驾驶员行为特性的目的。 The vehicle collision avoidance warning system according to claim 4, wherein: based on historical data of a particular type, using the driver releases the accelerator pedal frequency time division hundreds of bits collision time corresponding to 5% TTCA, the brakes start hundreds of time frequency division pedal position TTCb impact time and the reaction time corresponding to 5% of '3 as the characteristic parameter values ​​for each cluster, in the apparatus after the collision warning classification operation, changing in real time the result of the threshold value calculation formula, to achieve real-time attention the purpose behavioral characteristics of the driver.
6.如权利要求4所述的车辆防撞预警系统,其特征在于:所述自学习功能模块需要两个步骤,首先完成BP神经网络驾驶员类型判断模块,然后获取驾驶员释放加速踏板时刻的统计聚类百分频数为5%对应的碰撞时间TTCa、启动刹车踏板时刻的统计聚类百分频数为5%对应的碰撞时间TTCb、驾驶员反应时间';TTCa求法是:在车辆处于跟驰状态时,设车距为D,前后两车的相对速度为Δν,则碰撞时间;经实验研究证明,J7u体现了驾驶员对追尾危险的判断机制,与潜在追尾危险的紧急程度相关;根据驾驶员跟车数据序列,提取出激进、普通、保守三种类型驾驶员释放加速踏板时刻累积ITU的频数,并取每种类型对应频数5%的值为该类型的TTCa ; TTCb的求法与TTCa的求法相同。 The vehicle collision avoidance warning system according to claim 4, wherein: the self-learning module requires two steps, the first driver to complete BP neural network type determining module, and acquires the time the driver releases the accelerator pedal statistical clustering percentages frequency corresponding to 5% of the time to collision TTCa, statistical clustering start time of the brake pedal percentage corresponding to the frequency of 5% of the time to collision TTCb, the reaction time of the driver '; TTCa seeking method is: in a vehicle when the car following state, set distance between vehicles is D, the relative speed of the two vehicles before and after is Δν, the collision time; experimental studies have proved that, J7u mechanism reflects the judgment of the driver's rear-end collision risk, and the potential danger of rear-end related to the degree of urgency; the driver of the car with the data sequence extracted radical, general, three types of conserved driver releases the accelerator pedal in time the cumulative frequency of the ITU, and taking each type corresponding to 5% of the frequency of this type is TTCa; TTCB method for finding and TTCa method of calculating the same.
7.如权利要求4所述的车辆防撞预警系统,其特征在于:所述车辆碰撞判断模块采用车辆之间通过DSRC车车通信技术实现本车SV和周围车辆NV交换车辆状态信息,根据车辆的相对位置和相对运动趋势对可能发生的轨迹碰撞划分为纵向防撞、侧向防撞两大类,每类适用不同的阈值计算方法;防撞预警信号分为三级,分别为一级提醒、二级警告和三级报Sfc目。 The vehicle collision avoidance warning system according to claim 4, wherein: the collision of the vehicle is determined to achieve the vehicle around the vehicle, and SV-vehicle communication by DSRC module using technology vehicle NV exchange between vehicle state information, according to the vehicle the relative position and relative movement trend possible collision trajectory is divided into longitudinal collision, side collision two categories, different threshold value calculation method is suitable for each class; collision warning signal is divided into three, namely an alert , two and three warning messages Sfc mesh.
8.如权利要求1所述的车辆防撞预警系统,其特征在于:所述防撞预警装置模块在汽车中控台安装一个显示屏,用示意图标注本车SV与可能碰撞车辆的相对位置,且安装能显示绿、黄、红三种颜色的信号指示灯,显示屏旁设置蜂鸣报警器;预警信号划分为三个级别, 级别越高,情况越紧急,其中一级提醒方式为绿灯闪烁,二级警告方式为黄灯闪烁,三级报警方式为红灯闪烁外加蜂鸣报警声。 The vehicle collision avoidance warning system according to claim 1, wherein: said collision warning device module mounted on a display console car, schematically denoted by SV relative position of the vehicle may collide with the vehicle, and is mounted to show green, yellow, and red signal lamp, a buzzer alarm is provided next to the display; warning signal is divided into three levels, the higher the level, the more an emergency situation, a prompt mode wherein the green light flashes , two warning mode is blinking, the alarm mode of three plus red light flashing alarm buzzer.
9.权利要求1至8中任一权利要求所述车辆防撞预警系统的用途,其特征在于:该车辆防撞预警系统在实现车辆防撞分级预警功能中的应用。 9. The process according to any one of claim 1 to 8, the use of claim collision warning system of the vehicle, wherein: the vehicle collision warning classification application implements functions of the vehicle collision warning systems. \ \
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