技术领域technical field
本发明属于车载雷达智能泊车领域，特别涉及一种基于毫米波雷达成像的无人驾驶汽车智能泊车方法。The invention belongs to the field of vehicle-mounted radar intelligent parking, and in particular relates to an intelligent parking method for unmanned vehicles based on millimeter-wave radar imaging.
背景技术Background technique
随着城市的现代化发展，越来越来领域引入自动驾驶技术，无人驾驶汽车的安全性问题受到越来越来的关注。无人驾驶汽车的泊车过程是指依靠传感器装置和全球定位系统协同合作，提供安全可靠的泊车环境信息，推荐合理的泊车计划。其主要由环境感知系统、路径规划系统、运动控制系统等组成。环境感知环节通过各种传感器采集周围环境的基本信息，是自动驾驶的基础，主要包括以下几种方式：光学摄像头、超声波检测器、红外检测器、激光雷达、毫米波雷达。主要存在以下问题：(1)光学摄像头极大地受天气和光照条件的影响，且受障碍物的制约；(2)红外及超声波传感器受污物、遮盖物影响大，传感距离有限，精度准确度较差；(3)激光雷达的计算复杂度较高，且其结构笨重，价格昂贵，目前还没有得到广泛的应用；(4)毫米波雷达通过波形的频率差计算障碍物的距离，无法提供可视性界面，无法得到直观的车位占用情况。With the modernization of cities and the introduction of autonomous driving technology in more and more fields, the safety of driverless cars has received more and more attention. The parking process of driverless cars refers to relying on the cooperation of sensor devices and global positioning systems to provide safe and reliable parking environment information and recommend reasonable parking plans. It is mainly composed of environment perception system, path planning system, motion control system and so on. The environmental perception link collects the basic information of the surrounding environment through various sensors, which is the basis of automatic driving. It mainly includes the following methods: optical camera, ultrasonic detector, infrared detector, laser radar, and millimeter wave radar. The main problems are as follows: (1) The optical camera is greatly affected by weather and light conditions, and is restricted by obstacles; (2) Infrared and ultrasonic sensors are greatly affected by dirt and coverings, and the sensing distance is limited and the accuracy is accurate (3) The computational complexity of lidar is high, and its structure is cumbersome and expensive, and it has not been widely used at present; (4) millimeter-wave radar calculates the distance of obstacles through the frequency difference of the waveform, which cannot A visual interface is provided, and it is impossible to obtain an intuitive parking space occupancy situation.
基于雷达传感器的辅助泊车技术，相比于其他传感方法，可以实现对目标的探测、测距、测角、测速、跟踪、成像等，另外它具有对环境影响不敏感，抗干扰能力强，可同时探测多车位等优点。特别的，毫米波合成孔径雷达(SAR)具有全天候、全天候工作和强大的高分辨率成像功能，可用于汽车应用的实时成像，提供稳定、全面的实时停车场景图，是获取停车信息的一种很好的方式。Compared with other sensing methods, the auxiliary parking technology based on radar sensors can realize target detection, ranging, angle measurement, speed measurement, tracking, imaging, etc. In addition, it is not sensitive to environmental influences and has strong anti-interference ability , can detect multiple parking spaces at the same time and so on. In particular, millimeter-wave synthetic aperture radar (SAR) has all-weather, all-weather work and powerful high-resolution imaging capabilities, which can be used for real-time imaging in automotive applications, providing a stable and comprehensive real-time parking scene map, and is a method for obtaining parking information good way.
发明内容Contents of the invention
本发明的目的在于克服现有技术的不足，提供一种不受天气和光照条件的影响，不受障碍物的制约，抗干扰能力强，可以同时探测多车位，提供可视性界面，能够有效地提供稳定可靠车位占用信息及给出灵活的泊车计划的基于毫米波雷达成像的无人驾驶汽车智能泊车方法。The purpose of the present invention is to overcome the deficiencies of the prior art, to provide a car that is not affected by weather and light conditions, is not restricted by obstacles, has strong anti-interference ability, can detect multiple parking spaces at the same time, provides a visual interface, and can effectively An intelligent parking method for unmanned vehicles based on millimeter-wave radar imaging that provides stable and reliable parking space occupancy information and flexible parking plans.
本发明的目的是通过以下技术方案来实现的：基于毫米波雷达成像的无人驾驶汽车智能泊车方法，包括以下步骤：The object of the present invention is achieved through the following technical solutions: the intelligent parking method for unmanned vehicles based on millimeter-wave radar imaging, comprising the following steps:
S1、获取停车场景SAR图像；S1. Acquire the SAR image of the parking scene;
S2、提取停车场景中的已停放车辆；S2. Extracting the parked vehicles in the parking scene;
S3、定位可用停车位；S3, positioning available parking spaces;
S4、规划泊车路线，根据步骤S1～S3检测到的场景中的车位占用情况，选择可行泊车起始点，选取满足车辆最小转弯半径约束和泊车避障约束的两圆弧相切曲泊车轨迹，最终完成辅助停车。S4. Planning the parking route. According to the occupancy of parking spaces in the scene detected in steps S1-S3, select a feasible starting point for parking, and select a two-arc tangent curved parking that satisfies the minimum turning radius constraint of the vehicle and the parking obstacle avoidance constraint. trajectories, eventually completing assisted parking.
进一步地，所述步骤S1具体实现方法为：Further, the specific implementation method of the step S1 is:
S11、对接收到的回波数据做一维傅里叶变换，完成对接收脉冲的距离脉压，获得距离向的高分辨特性：S11. Perform one-dimensional Fourier transform on the received echo data, complete the distance pulse pressure of the received pulse, and obtain the high-resolution characteristics of the distance direction:
secho＝fft(prec) (1)secho = fft(prec ) (1)
其中，prec表示接收的脉冲信号，fft(·)表示一维傅里叶变换，secho表示距离脉压后的信号；Among them, prec represents the received pulse signal, fft( ) represents the one-dimensional Fourier transform, and secho represents the signal after the pulse pressure;
S12、针对雷达平台与目标相对位置变化而产生的距离单元徙动现象，对脉冲压缩后的信号进行运动补偿，完成距离单元徙动校正：S12. For the range unit migration phenomenon caused by the relative position change between the radar platform and the target, perform motion compensation on the signal after pulse compression, and complete the range unit migration correction:
其中，表示相位补偿因子，fft2和ifft2分别代表二维傅里叶变换和二维傅里叶逆变换，srcmc表示距离徙动校正后的信号；in, Represents the phase compensation factor, fft2 and ifft2 represent two-dimensional Fourier transform and two-dimensional inverse Fourier transform respectively, srcmc represents the signal after range migration correction;
S13、对距离单元徙动校正后的信号进行方位脉压，得到高分辨率的合成孔径雷达图像：S13. Perform azimuth pulse pressure on the signal after the migration correction of the range unit to obtain a high-resolution synthetic aperture radar image:
其中，sout为输出的高分辨率SRA图像，hfilter表示匹配滤波器。Among them, sout is the output high-resolution SRA image, and hfilter represents the matched filter.
进一步地，所述步骤S2包括以下子步骤：Further, the step S2 includes the following sub-steps:
S21、对所获取毫米波合成孔径雷达图像，利用最大极值稳定区域方法提取场景中已停放车辆区域；判断极值区域的面积变化率是否满足若是，将该区域认为是稳定区域，即已停放车辆区域，否则该区域认为没有停放车辆；得到区域集合为：S21. For the acquired millimeter-wave synthetic aperture radar image, use the maximum extreme value stable area method to extract the parked vehicle area in the scene; determine the area change rate of the extreme value area Is it satisfied If so, the area is considered to be a stable area, that is, the area of parked vehicles, otherwise the area is considered to have no parked vehicles; the obtained area set is:
其中，为极值区域，{ni|ni＝ni+1-Δ}；Δ为灰度稳定范围，S(·)为区域面积，R为区域集合，为极值区域的面积变化率，ε为面积变化率的上限；in, is the extreme value area, {ni |ni =ni+1 -Δ}; Δ is the gray stable range, S( ) is the area of the area, R is the set of areas, is the area change rate of the extreme value region, ε is the upper limit of the area change rate;
S22、使用面积、长宽比来消除步骤S21检测到的区域中对其他背景目标和强杂波的虚警，并利用上下文信息的差异来区分真实车辆与虚警；S22, using area and aspect ratio to eliminate false alarms to other background targets and strong clutter in the region detected in step S21, and using the difference in context information to distinguish real vehicles from false alarms;
具体实现方法为：依次计算连续区域的面积和长宽比，符合公式(5)(6)的区域被认为是车辆区域，不符合的区域被认为是虚警；The specific implementation method is: sequentially calculate the area and aspect ratio of the continuous area, the area that meets the formula (5) (6) is considered as the vehicle area, and the area that does not meet is considered as a false alarm;
然后，计算所有目标区域的中心点坐标，取平均值提取分割线，测量中心点到分割线的最小距离，根据公式(7)将距离大于阈值的目标确定为虚警目标，并从检测结果中剔除：Then, calculate the center point coordinates of all target areas, take the average value to extract the dividing line, measure the minimum distance from the center point to the dividing line, and determine the target with a distance greater than the threshold as a false alarm target according to the formula (7), and from the detection result Eliminate:
其中，R1、R2、R3分别代表候选车辆区域的集合，a,b为面积大小的范围，rWH表示长宽比，W(·)和L(·)分别为区域的宽度和长度，c,d表示长宽比的范围，Cy(·)为区域中心的y轴坐标，average[·]代表取平均值运算，e表示选取的阈值。Among them, R1 , R2 , and R3 respectively represent the collection of candidate vehicle regions, a, b are the area size range, rWH represents the aspect ratio, W(·) and L(·) are the width and length of the region respectively , c, d indicate the range of aspect ratio, Cy (·) is the y-axis coordinate of the center of the area, average[·] represents the average value operation, and e represents the selected threshold.
进一步地，所述步骤S3包括以下子步骤：Further, the step S3 includes the following sub-steps:
S31、根据步骤S2中提取的场景中已停放车辆的相对质心位置，确定两车之间的空间；然后通过已停放车辆的长宽比，判断停车方式是平行停车还是垂直停车，如式(8)所示：S31, according to the relative center of mass position of the parked vehicle in the scene extracted in step S2, determine the space between the two vehicles; then by the aspect ratio of the parked vehicle, judge whether the parking mode is parallel parking or vertical parking, such as formula (8 ) as shown:
计算两车之间的空间是否足够停车，以及能够容纳几辆车，利用式(9)得到两车之间可容纳的车辆数：Calculate whether the space between the two cars is enough to park, and how many cars can be accommodated, and use formula (9) to get the number of vehicles that can be accommodated between the two cars:
其中，wp和lv分别代表车位的宽度和长度，Cx(·)为区域中心的x轴坐标，floor[·]代表向下取整运算，num为两辆已停放车辆中间可容纳的车辆数；Among them, wp and lv represent the width and length of the parking space respectively, Cx ( ) is the x-axis coordinate of the center of the area, floor[ ] represents the rounding down operation, and num is the space that can be accommodated between two parked vehicles number of vehicles;
S32、采用MSER方法来评估车位中是否有影响停车的障碍物，将有障碍区的车位移除，获得最终的空闲停车位。S32. Using the MSER method to evaluate whether there is an obstacle affecting parking in the parking space, remove the parking space in the obstacle area, and obtain the final free parking space.
进一步地，所述步骤S4具体实现方法为：以当前车辆位置为泊车起点，若存在连接车辆泊车起始点和终止点且满足车辆最小转弯半径约束和泊车避障约束的两圆弧相切曲线，则该起始点为可行泊车起始点，选取满足下式的转弯半径R，给出持续稳定的泊车路径：Further, the specific implementation method of step S4 is: taking the current vehicle position as the starting point of parking, if there are two arcs that connect the starting point and the ending point of the vehicle parking and satisfy the vehicle minimum turning radius constraint and parking obstacle avoidance constraint curve, then the starting point is the feasible starting point of parking, and the turning radius R satisfying the following formula is selected to give a continuous and stable parking path:
对于平行泊车：For parallel parking:
R≥Rmin (10)R≥Rmin (10)
其中，Rmin代表车辆最小转弯半径，为与目标停车位相邻的前车后车的中心距离，Dcar为与目标停车位相邻的前车对角线长度，Wcar为装载雷达的车辆的宽度，为与目标停车位相邻的后车长度；Among them, Rmin represents the minimum turning radius of the vehicle, is the center distance of the front car and the rear car adjacent to the target parking space, Dcar is the diagonal length of the front car adjacent to the target parking space, Wcar is the width of the vehicle loaded with radar, is the length of the rear vehicle adjacent to the target parking space;
对于垂直泊车：For perpendicular parking:
R≥Rmin (13)R≥Rmin (13)
其中，Wcar和Lcar分别为装载雷达的车辆的宽度和长度，和分别为目标停车位的右边界线和左边界线的横坐标。Among them, Wcar and Lcar are the width and length of the vehicle loaded with radar, respectively, and are the abscissas of the right boundary line and left boundary line of the target parking space, respectively.
本发明的有益效果是：本发明的无人驾驶汽车智能泊车方法不受天气和光照条件的影响，不受障碍物的制约，抗干扰能力强，可以同时探测多车位，提供可视性界面，能够有效地提供稳定可靠车位占用信息及给出灵活的泊车计划。The beneficial effects of the present invention are: the intelligent parking method for unmanned vehicles of the present invention is not affected by weather and light conditions, is not restricted by obstacles, has strong anti-interference ability, can detect multiple parking spaces at the same time, and provides a visual interface , can effectively provide stable and reliable parking space occupancy information and give flexible parking plans.
附图说明Description of drawings
图1为本发明方法流程图。Fig. 1 is a flow chart of the method of the present invention.
具体实施方式Detailed ways
下面结合附图进一步说明本发明的技术方案。The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.
如图1所示，本发明的一种基于毫米波雷达成像的无人驾驶汽车智能泊车方法，包括以下步骤：As shown in Figure 1, a kind of unmanned vehicle intelligent parking method based on millimeter-wave radar imaging of the present invention comprises the following steps:
S1、获取停车场景SAR图像；具体实现方法为：S1. Obtain the SAR image of the parking lot scene; the specific implementation method is:
S11、对接收到的回波数据做一维傅里叶变换，完成对接收脉冲的距离脉压，获得距离向的高分辨特性：S11. Perform one-dimensional Fourier transform on the received echo data, complete the distance pulse pressure of the received pulse, and obtain the high-resolution characteristics of the distance direction:
secho＝fft(prec) (1)secho = fft(prec ) (1)
其中，prec表示接收的脉冲信号，fft(·)表示一维傅里叶变换，secho表示距离脉压后的信号；Among them, prec represents the received pulse signal, fft( ) represents the one-dimensional Fourier transform, and secho represents the signal after the pulse pressure;
S12、针对雷达平台与目标相对位置变化而产生的距离单元徙动现象，对脉冲压缩后的信号进行运动补偿，完成距离单元徙动校正：S12. For the range unit migration phenomenon caused by the relative position change between the radar platform and the target, perform motion compensation on the signal after pulse compression, and complete the range unit migration correction:
其中，表示相位补偿因子，fft2和ifft2分别代表二维傅里叶变换和二维傅里叶逆变换，srcmc表示距离徙动校正后的信号；in, Represents the phase compensation factor, fft2 and ifft2 represent two-dimensional Fourier transform and two-dimensional inverse Fourier transform respectively, srcmc represents the signal after range migration correction;
S13、对距离单元徙动校正后的信号进行方位脉压，得到高分辨率的合成孔径雷达图像：S13. Perform azimuth pulse pressure on the signal after the migration correction of the range unit to obtain a high-resolution synthetic aperture radar image:
其中，sout为输出的高分辨率SRA图像，hfilter表示匹配滤波器。Among them, sout is the output high-resolution SRA image, and hfilter represents the matched filter.
S2、提取停车场景中的已停放车辆；包括以下子步骤：S2. Extracting the parked vehicle in the parking scene; including the following sub-steps:
S21、对所获取毫米波合成孔径雷达图像，利用最大极值稳定区域(MSER)方法提取场景中已停放车辆区域；判断极值区域的面积变化率是否满足若是，将该区域认为是稳定区域，即已停放车辆区域，否则该区域认为没有停放车辆；得到区域集合为：S21. For the acquired millimeter-wave synthetic aperture radar image, use the maximum extremum stable region (MSER) method to extract the parked vehicle area in the scene; determine the area change rate of the extremum area Is it satisfied If so, the area is considered to be a stable area, that is, the area of parked vehicles, otherwise the area is considered to have no parked vehicles; the obtained area set is:
其中，为极值区域，{ni|ni＝ni+1-Δ}；Δ为灰度稳定范围，S(·)为区域面积，R为区域集合，为极值区域的面积变化率，ε为面积变化率的上限；in, is the extreme value area, {ni |ni =ni+1 -Δ}; Δ is the gray stable range, S( ) is the area of the area, R is the set of areas, is the area change rate of the extreme value region, ε is the upper limit of the area change rate;
S22、使用面积、长宽比来消除步骤S21检测到的区域中对其他背景目标和强杂波的虚警，并利用上下文信息的差异来区分真实车辆与虚警；S22, using area and aspect ratio to eliminate false alarms to other background targets and strong clutter in the region detected in step S21, and using the difference in context information to distinguish real vehicles from false alarms;
具体实现方法为：依次计算连续区域的面积和长宽比，符合公式(5)(6)的区域被认为是车辆区域，不符合的区域被认为是虚警；The specific implementation method is: sequentially calculate the area and aspect ratio of the continuous area, the area that meets the formula (5) (6) is considered as the vehicle area, and the area that does not meet is considered as a false alarm;
然后，计算所有目标区域的中心点坐标，取平均值提取分割线，测量中心点到分割线的最小距离，根据公式(7)将距离大于阈值的目标确定为虚警目标，并从检测结果中剔除：Then, calculate the center point coordinates of all target areas, take the average value to extract the dividing line, measure the minimum distance from the center point to the dividing line, and determine the target with a distance greater than the threshold as a false alarm target according to the formula (7), and from the detection result Eliminate:
其中，R1、R2、R3分别代表候选车辆区域的集合，a,b为面积大小的范围，rWH表示长宽比，W(·)和L(·)分别为区域的宽度和长度，c,d表示长宽比的范围，Cy(·)为区域中心的y轴坐标，average[·]代表取平均值运算，e表示选取的阈值。Among them, R1 , R2 , and R3 respectively represent the collection of candidate vehicle regions, a, b are the area size range, rWH represents the aspect ratio, W(·) and L(·) are the width and length of the region respectively , c, d indicate the range of aspect ratio, Cy (·) is the y-axis coordinate of the center of the area, average[·] represents the average value operation, and e represents the selected threshold.
S3、定位可用停车位；包括以下子步骤：S3. Locating an available parking space; including the following sub-steps:
S31、根据步骤S2中提取的场景中已停放车辆的相对质心位置，确定两车之间的空间；然后通过已停放车辆的长宽比，判断停车方式是平行停车还是垂直停车，如式(8)所示：S31, according to the relative center of mass position of the parked vehicle in the scene extracted in step S2, determine the space between the two vehicles; then by the aspect ratio of the parked vehicle, judge whether the parking mode is parallel parking or vertical parking, such as formula (8 ) as shown:
计算两车之间的空间是否足够停车，以及能够容纳几辆车，利用式(9)得到两车之间可容纳的车辆数：Calculate whether the space between the two cars is enough to park, and how many cars can be accommodated, and use formula (9) to get the number of vehicles that can be accommodated between the two cars:
其中，wp和lv分别代表车位的宽度和长度，Cx(·)为区域中心的x轴坐标，floor[·]代表向下取整运算，num为两辆已停放车辆中间可容纳的车辆数；Among them, wp and lv represent the width and length of the parking space respectively, Cx ( ) is the x-axis coordinate of the center of the area, floor[ ] represents the rounding down operation, and num is the space that can be accommodated between two parked vehicles number of vehicles;
S32、采用MSER方法来评估车位中是否有影响停车的障碍物，将有障碍区的车位移除，获得最终的空闲停车位。S32. Using the MSER method to evaluate whether there is an obstacle affecting parking in the parking space, remove the parking space in the obstacle area, and obtain the final free parking space.
S4、规划泊车路线，根据步骤S1～S3检测到的场景中的车位占用情况，选择可行泊车起始点，选取满足车辆最小转弯半径约束和泊车避障约束的两圆弧相切曲泊车轨迹，最终完成辅助停车；具体实现方法为：具体实现方法为：以当前车辆位置为泊车起点，若存在连接车辆泊车起始点和终止点且满足车辆最小转弯半径约束和泊车避障约束的两圆弧相切曲线，则该起始点为可行泊车起始点，选取满足下式的转弯半径R，给出持续稳定的泊车路径：S4. Planning the parking route. According to the occupancy of parking spaces in the scene detected in steps S1-S3, select a feasible starting point for parking, and select a two-arc tangent curved parking that satisfies the minimum turning radius constraint of the vehicle and the parking obstacle avoidance constraint. The specific implementation method is as follows: take the current vehicle position as the starting point of parking, if there is a vehicle that connects the starting point and end point of parking and satisfies the minimum turning radius constraint and parking obstacle avoidance constraint If the two arcs are tangent curves, then the starting point is the feasible starting point for parking, and the turning radius R satisfying the following formula is selected to give a continuous and stable parking path:
对于平行泊车：For parallel parking:
R≥Rmin (10)R≥Rmin (10)
其中，Rmin代表车辆最小转弯半径，为与目标停车位相邻的前车后车的中心距离，Dcar为与目标停车位相邻的前车对角线长度，Wcar为装载雷达的车辆的宽度，为与目标停车位相邻的后车长度；Among them, Rmin represents the minimum turning radius of the vehicle, is the center distance of the front car and the rear car adjacent to the target parking space, Dcar is the diagonal length of the front car adjacent to the target parking space, Wcar is the width of the vehicle loaded with radar, is the length of the rear vehicle adjacent to the target parking space;
对于垂直泊车：For perpendicular parking:
R≥Rmin (13)R≥Rmin (13)
其中，Wcar和Lcar分别为装载雷达的车辆的宽度和长度，和分别为目标停车位的右边界线和左边界线的横坐标。Among them, Wcar and Lcar are the width and length of the vehicle loaded with radar, respectively, and are the abscissas of the right boundary line and left boundary line of the target parking space, respectively.
本领域的普通技术人员将会意识到，这里所述的实施例是为了帮助读者理解本发明的原理，应被理解为本发明的保护范围并不局限于这样的特别陈述和实施例。本领域的普通技术人员可以根据本发明公开的这些技术启示做出各种不脱离本发明实质的其它各种具体变形和组合，这些变形和组合仍然在本发明的保护范围内。Those skilled in the art will appreciate that the embodiments described here are to help readers understand the principles of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical revelations disclosed in the present invention without departing from the essence of the present invention, and these modifications and combinations are still within the protection scope of the present invention.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910674959.6ACN110379178B (en) | 2019-07-25 | 2019-07-25 | Intelligent parking method for unmanned vehicles based on millimeter wave radar imaging |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910674959.6ACN110379178B (en) | 2019-07-25 | 2019-07-25 | Intelligent parking method for unmanned vehicles based on millimeter wave radar imaging |
| Publication Number | Publication Date |
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| CN110379178Atrue CN110379178A (en) | 2019-10-25 |
| CN110379178B CN110379178B (en) | 2021-11-02 |
| Application Number | Title | Priority Date | Filing Date |
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| CN201910674959.6AActiveCN110379178B (en) | 2019-07-25 | 2019-07-25 | Intelligent parking method for unmanned vehicles based on millimeter wave radar imaging |
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