CN117783572A - A self-vehicle speed estimation method, system and platform suitable for multiple scenarios - Google Patents

A self-vehicle speed estimation method, system and platform suitable for multiple scenarios Download PDF

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CN117783572A
CN117783572A CN202311755974.6A CN202311755974A CN117783572A CN 117783572 A CN117783572 A CN 117783572A CN 202311755974 A CN202311755974 A CN 202311755974A CN 117783572 A CN117783572 A CN 117783572A
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CN117783572B (en
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陈承文
周珂
何也
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Suzhou Chengtai Technology Co ltd
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Abstract

本发明公开了一种适用于多场景的自车速度估测方法、系统及平台,通过获取场景中与自车相对应检测点的第一数据,根据所述第一数据生成与第一数据相对应的自车纵向速度;其中,所述第一数据包括检测点距离数据、检测点速度数据和检测点角度数据;根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况;其中,所述第二数据包括自车车速数据、自车偏转角数据、自车纵向加速度数据和自车轮速数据;根据所述静止点速度分布情况,实时生成与自车身相对应的自车速度数据,以及和方法相对应的系统、平台,以实现实时获取自车的准确速度的效果,即在自车加速度较大的情况下(起步、急刹等)获取更加准确的车速。

The present invention discloses a self-vehicle speed estimation method, system and platform applicable to multiple scenarios. The method obtains first data of a detection point corresponding to the self-vehicle in the scene, and generates the self-vehicle longitudinal speed corresponding to the first data according to the first data; wherein the first data includes detection point distance data, detection point speed data and detection point angle data; according to the self-vehicle longitudinal speed, in combination with second data corresponding to the self-vehicle body, generates the static point speed distribution corresponding to the self-vehicle body; wherein the second data includes self-vehicle speed data, self-vehicle deflection angle data, self-vehicle longitudinal acceleration data and self-vehicle wheel speed data; according to the static point speed distribution, generates the self-vehicle speed data corresponding to the self-vehicle body in real time, and the system and platform corresponding to the method, so as to achieve the effect of obtaining the accurate speed of the self-vehicle in real time, that is, obtaining a more accurate vehicle speed when the acceleration of the self-vehicle is large (starting, emergency braking, etc.).

Description

一种适用于多场景的自车速度估测方法、系统及平台A self-driving vehicle speed estimation method, system and platform suitable for multiple scenarios

技术领域Technical field

本发明属于车速度估测处理技术领域,具体涉及一种适用于多场景的自车速度估测方法、系统及平台。The invention belongs to the technical field of vehicle speed estimation and processing, and specifically relates to a self-vehicle speed estimation method, system and platform suitable for multiple scenarios.

背景技术Background technique

毫米波雷达是ADAS中的关键传感器之一,其基础功能包括距离测量、速度测量和角度测量,由于毫米波雷达的测速原理,测得的速度属于相对速度,在汽车级应用场景中,需要根据测速信息和自车车速信息才能判断目标的运动属性。Millimeter wave radar is one of the key sensors in ADAS. Its basic functions include distance measurement, speed measurement and angle measurement. Due to the speed measurement principle of millimeter wave radar, the measured speed is relative speed. In automotive-grade application scenarios, it needs to be based on Only the speed measurement information and self-vehicle speed information can be used to determine the movement attributes of the target.

现有的自车车速信息都是通过车身信息获取,存在一定的延时,在自车正常行驶过程中延迟影响不大,但在个别场景如急加减速时,获取的自车车速信息与实际车速有较大差异,会导致目标运动状态判定的错误,进而影响后续融合或功能的实现。The existing self-vehicle speed information is obtained from the body information, and there is a certain delay. The delay has little effect during the normal driving process of the self-vehicle. However, in certain scenarios such as sudden acceleration and deceleration, the obtained self-vehicle speed information is different from the actual speed information. Large differences in vehicle speed will lead to errors in determining the target motion state, which will affect subsequent fusion or function realization.

此外,对于专利CN111308458A一种基于车载毫米波雷达的自车速度估计方法,通过获取所有目标的距离、速度和角度,并计算目标与自车车辆法线方向的相对速度;统计所有静止目标的速度,进行置信度匹配,得到目标数目排名前三的目标簇结果;根据目标簇中的速度估计置信度估计自车速度。该方案公开的是基于静止目标的速度统计并运用平滑滤波处理减小速度估计误差,但其未考虑目标车辆的车身姿态,计算所得法线方向的相对速度不准确,而且在拥堵缓行或空旷等静止目标较少的场景下无法获取有效的静止目标簇。In addition, for patent CN111308458A, a self-vehicle speed estimation method based on vehicle-mounted millimeter wave radar, by obtaining the distance, speed and angle of all targets, and calculating the relative speed of the target and the normal direction of the self-vehicle vehicle; counting the speed of all stationary targets , conduct confidence matching, and obtain the target cluster results with the top three target numbers; estimate the self-vehicle speed based on the speed estimation confidence in the target cluster. This solution disclosed is based on the speed statistics of stationary targets and uses smoothing filtering to reduce the speed estimation error. However, it does not consider the body posture of the target vehicle, and the calculated relative speed in the normal direction is inaccurate. Moreover, it is not accurate when driving slowly in congestion or in the open space. Effective stationary target clusters cannot be obtained in scenarios with fewer stationary targets.

因此,针对以上的技术问题缺陷,急需设计和开发一种适用于多场景的自车速度估测方法、系统及平台。Therefore, in view of the above technical problems and defects, it is urgent to design and develop a self-driving vehicle speed estimation method, system and platform suitable for multiple scenarios.

发明内容Summary of the invention

为克服上述现有技术存在的不足及困难,本发明提供一种适用于多场景的自车速度估测方法、系统及平台,以实现实时获取自车的准确速度的效果,即可以在自车加速度较大的情况下(起步、急刹等)获取更加准确的车速。In order to overcome the shortcomings and difficulties in the above-mentioned existing technologies, the present invention provides a self-vehicle speed estimation method, system and platform suitable for multiple scenarios, so as to achieve the effect of obtaining the accurate speed of the self-vehicle in real time, that is, it can be used in the self-vehicle Obtain more accurate vehicle speed in situations of large acceleration (starting, sudden braking, etc.).

本发明的第一目的在于提供一种适用于多场景的自车速度估测方法;本发明的第二目的在于提供一种适用于多场景的自车速度估测系统;本发明的第三目的在于提供一种适用于多场景的自车速度估测平台。The first purpose of the present invention is to provide a self-vehicle speed estimation method suitable for multiple scenarios; the second purpose of the present invention is to provide a self-vehicle speed estimation system suitable for multiple scenarios; and the third purpose of the present invention The aim is to provide a self-vehicle speed estimation platform suitable for multiple scenarios.

本发明的第一目的是这样实现的:The first purpose of the present invention is achieved in this way:

获取场景中与自车相对应检测点的第一数据,根据所述第一数据生成与第一数据相对应的自车纵向速度;其中,所述第一数据包括检测点距离数据、检测点速度数据和检测点角度数据;Obtain the first data of the detection point corresponding to the self-vehicle in the scene, and generate the longitudinal speed of the self-vehicle corresponding to the first data according to the first data; wherein the first data includes detection point distance data, detection point speed Data and detection point angle data;

根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况;其中,所述第二数据包括自车车速数据、自车偏转角数据、自车纵向加速度数据和自车轮速数据;According to the longitudinal speed of the self-vehicle, combined with the second data corresponding to the self-vehicle body, a stationary point velocity distribution corresponding to the self-vehicle body is generated; wherein the second data includes the self-vehicle speed data and the self-vehicle deflection angle data. , vehicle longitudinal acceleration data and vehicle wheel speed data;

根据所述静止点速度分布情况,实时生成与自车身相对应的自车速度数据。According to the static point speed distribution, the vehicle speed data corresponding to the vehicle body is generated in real time.

进一步地,所述自车纵向速度的计算公式如下所示:Further, the calculation formula of the vehicle's longitudinal speed is as follows:

Votg=V/cosθ (1)V otg =V/cosθ (1)

其中,Votg纵为纵向速度;V为检测点的径向速度,θ为检测点的方位角。Among them, V otg is the longitudinal velocity; V is the radial velocity of the detection point, and θ is the azimuth angle of the detection point.

进一步地,所述根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况,还包括:Further, generating a stationary point velocity distribution corresponding to the self-body according to the longitudinal speed of the self-vehicle, combined with the second data corresponding to the self-body, further includes:

创建与检测点动静属性相对应的第一门限阈值;其中,所述第一门限阈值为用于判断检测点动静属性门限;Create a first threshold corresponding to the motion and static attributes of the detection point; wherein the first threshold is a threshold for judging the motion and static attributes of the detection point;

判定生成与所述自车纵向速度相对应且小于所述第一门限阈值的第一检测点数据。It is determined that first detection point data corresponding to the longitudinal speed of the vehicle and smaller than the first threshold value is generated.

进一步地,所述第一门限阈值的计算公式如下所示:Further, the calculation formula of the first threshold is as follows:

Threshold1=Verr+cosθ·lon+sinθ·lat (2)Threshold1= Verr +cosθ·lon+sinθ·lat (2)

其中,Verr为最小基线误差,lon为纵向速度修正,lat为横向速度修正。Among them, V err is the minimum baseline error, lon is the longitudinal speed correction, and lat is the transverse speed correction.

进一步地,所述根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况,还包括:Further, generating a stationary point velocity distribution corresponding to the self-body according to the longitudinal speed of the self-vehicle, combined with the second data corresponding to the self-body, further includes:

根据自车场景情况,创建与检测点动静属性相对应的第二门限阈值;其中,所述第二门限阈值为满足第一门限阈值的有效点数量门限;According to the vehicle scene, a second threshold corresponding to the dynamic and static attributes of the detection point is created; wherein the second threshold is a threshold of the number of valid points that meets the first threshold;

判定生成与所述自车纵向速度相对应且小于所述第二门限阈值的第二检测点数据。It is determined to generate second detection point data corresponding to the longitudinal speed of the own vehicle and smaller than the second threshold value.

进一步地,所述根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况,还包括:Further, generating a stationary point velocity distribution corresponding to the self-body according to the longitudinal speed of the self-vehicle, combined with the second data corresponding to the self-body, further includes:

结合直方图统计,创建与直方图相对应的直方图参数;其中,所述直方图参数包括极差、组距和组数;Combined with histogram statistics, create histogram parameters corresponding to the histogram; wherein the histogram parameters include range, group distance and number of groups;

根据所述直方图参数,生成与检测点相对应的组别数据;其中,所述组别数据包括各个检测点所在组号;According to the histogram parameters, group data corresponding to the detection points are generated; wherein the group data includes the group number of each detection point;

根据所述组别数据,依次比较各个组别中的检测点个数,并生成与检测点个数相对应的组号数据;其中,所述组号数据为检测点个数组所在的组号。According to the group data, the number of detection points in each group is compared in turn, and group number data corresponding to the number of detection points is generated; wherein the group number data is the group number to which the detection point array belongs.

进一步地,所述根据所述静止点速度分布情况,实时生成与自车身相对应的自车速度数据,还包括:Further, generating self-vehicle speed data corresponding to the self-vehicle body in real time based on the stationary point speed distribution also includes:

结合线性滤波,对与自车身相对应的偏转角数据进行平滑处理;Combined with linear filtering, the deflection angle data corresponding to the self-body body is smoothed;

创建与自车纵向加速度相对应的第三门限阈值,并根据所述第三门限阈值判定自车速度数据是否存在异常;其中,所述第三门限阈值为自车最大纵向加速度的30%。Create a third threshold corresponding to the longitudinal acceleration of the own vehicle, and determine whether there is an abnormality in the speed data of the own vehicle based on the third threshold; wherein the third threshold is 30% of the maximum longitudinal acceleration of the own vehicle.

本发明的第二目的是这样实现的:所述系统应用于所述的自车速度估测方法;所述系统包括:The second object of the present invention is achieved in this way: the system is applied to the self-vehicle speed estimation method; the system includes:

获取生成单元,用于获取场景中与自车相对应检测点的第一数据,根据所述第一数据生成与第一数据相对应的自车纵向速度;其中,所述第一数据包括检测点距离数据、检测点速度数据和检测点角度数据;An acquisition generating unit is used to acquire first data of a detection point corresponding to the vehicle in the scene, and generate a longitudinal speed of the vehicle corresponding to the first data according to the first data; wherein the first data includes detection point distance data, detection point speed data and detection point angle data;

第一生成单元,用于根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况;其中,所述第二数据包括自车车速数据、自车偏转角数据、自车纵向加速度数据和自车轮速数据;A first generation unit configured to generate a stationary point velocity distribution corresponding to the self-vehicle body based on the longitudinal speed of the self-vehicle in combination with second data corresponding to the self-vehicle body; wherein the second data includes the self-vehicle speed Data, vehicle deflection angle data, vehicle longitudinal acceleration data and vehicle wheel speed data;

第二生成单元,用于根据所述静止点速度分布情况,实时生成与自车身相对应的自车速度数据。The second generation unit is used to generate the vehicle speed data corresponding to the vehicle body in real time according to the stationary point speed distribution.

进一步地,所述自车纵向速度的计算公式如下所示:Further, the calculation formula of the vehicle's longitudinal speed is as follows:

Votg=V/cosθ (1)V otg =V/cosθ (1)

其中,Votg纵为纵向速度;V为检测点的径向速度,θ为检测点的方位角;Among them, V otg is the longitudinal velocity; V is the radial velocity of the detection point, and θ is the azimuth angle of the detection point;

所述第一生成单元,还包括:The first generation unit also includes:

第一创建模块,用于创建与检测点动静属性相对应的第一门限阈值;其中,所述第一门限阈值为用于判断检测点动静属性门限;The first creation module is used to create a first threshold corresponding to the motion and static attributes of the detection point; wherein the first threshold is a threshold for judging the motion and static attributes of the detection point;

第一判定模块,用于判定生成与所述自车纵向速度相对应且小于所述第一门限阈值的第一检测点数据;A first determination module, configured to determine and generate first detection point data corresponding to the longitudinal speed of the own vehicle and less than the first threshold;

所述第一门限阈值的计算公式如下所示:The calculation formula of the first threshold value is as follows:

Threshold1=Verr+cosθ·lon+sinθ·lat (2)Threshold1=V err +cosθ·lon+sinθ·lat (2)

其中,Verr为最小基线误差,lon为纵向速度修正,lat为横向速度修正;Among them, V err is the minimum baseline error, lon is the longitudinal velocity correction, and lat is the transverse velocity correction;

和/或,所述第一生成单元,还包括:And/or, the first generation unit also includes:

第二创建模块,用于根据自车场景情况,创建与检测点动静属性相对应的第二门限阈值;其中,所述第二门限阈值为满足第一门限阈值的有效点数量门限;The second creation module is used to create a second threshold corresponding to the motion and static attributes of the detection point according to the situation of the self-vehicle scene; wherein the second threshold is a threshold of the number of valid points that satisfies the first threshold;

第二判定模块,用于判定生成与所述自车纵向速度相对应且小于所述第二门限阈值的第二检测点数据;A second determination module, configured to determine and generate second detection point data corresponding to the longitudinal speed of the own vehicle and less than the second threshold;

和/或,所述第一生成单元,还包括:And/or, the first generation unit also includes:

第三创建模块,用于结合直方图统计,创建与直方图相对应的直方图参数;其中,所述直方图参数包括极差、组距和组数;A third creation module is used to create histogram parameters corresponding to the histogram in combination with histogram statistics; wherein the histogram parameters include range, group interval and number of groups;

第一生成模块,用于根据所述直方图参数,生成与检测点相对应的组别数据;其中,所述组别数据包括各个检测点所在组号;The first generation module is used to generate group data corresponding to the detection points according to the histogram parameters; wherein the group data includes the group number of each detection point;

第二生成模块,用于根据所述组别数据,依次比较各个组别中的检测点个数,并生成与检测点个数相对应的组号数据;其中,所述组号数据为检测点个数组所在的组号;The second generation module is used to sequentially compare the number of detection points in each group according to the group data, and generate group number data corresponding to the number of detection points; wherein the group number data is the detection point The group number where the array is located;

和/或,所述第二生成单元,还包括:And/or, the second generation unit also includes:

第一处理模块,用于结合线性滤波,对与自车身相对应的偏转角数据进行平滑处理;The first processing module is used to combine linear filtering to smooth the deflection angle data corresponding to the self-body;

第三判定模块,用于创建与自车纵向加速度相对应的第三门限阈值,并根据所述第三门限阈值判定自车速度数据是否存在异常;其中,所述第三门限阈值为自车最大纵向加速度的30%。The third determination module is used to create a third threshold corresponding to the longitudinal acceleration of the own vehicle, and determine whether there is an abnormality in the speed data of the own vehicle according to the third threshold; wherein the third threshold is the maximum value of the own vehicle. 30% of longitudinal acceleration.

本发明的第三目的是这样实现的:包括处理器、存储器以及适用于多场景的自车速度估测平台控制程序;其中在所述的处理器执行所述的适用于多场景的自车速度估测平台控制程序,所述的适用于多场景的自车速度估测平台控制程序被存储在所述存储器中,所述的适用于多场景的自车速度估测平台控制程序,实现所述的适用于多场景的自车速度估测方法。The third object of the present invention is achieved by: including a processor, a memory and a self-vehicle speed estimation platform control program suitable for multiple scenarios; wherein the processor executes the self-vehicle speed estimation suitable for multiple scenarios Estimation platform control program, the self-vehicle speed estimation platform control program suitable for multiple scenarios is stored in the memory, the self-vehicle speed estimation platform control program suitable for multiple scenarios implements the A self-vehicle speed estimation method suitable for multiple scenarios.

本发明通过获取场景中与自车相对应检测点的第一数据,根据所述第一数据生成与第一数据相对应的自车纵向速度;其中,所述第一数据包括检测点距离数据、检测点速度数据和检测点角度数据;根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况;其中,所述第二数据包括自车车速数据、自车偏转角数据、自车纵向加速度数据和自车轮速数据;根据所述静止点速度分布情况,实时生成与自车身相对应的自车速度数据,以及和方法相对应的系统、平台,以实现实时获取自车的准确速度的效果,即可以在自车加速度较大的情况下(起步、急刹等)获取更加准确的车速。The present invention obtains the first data of the detection point corresponding to the self-vehicle in the scene, and generates the longitudinal speed of the self-vehicle corresponding to the first data according to the first data; wherein the first data includes detection point distance data, Detection point speed data and detection point angle data; according to the longitudinal speed of the own vehicle, combined with the second data corresponding to the own vehicle body, a stationary point velocity distribution corresponding to the own vehicle body is generated; wherein the second data includes Self-vehicle speed data, self-vehicle deflection angle data, self-vehicle longitudinal acceleration data and self-vehicle wheel speed data; according to the stationary point speed distribution, self-vehicle speed data corresponding to the self-vehicle body is generated in real time, as well as the self-vehicle speed data corresponding to the method. The system and platform can achieve the effect of obtaining the accurate speed of the own vehicle in real time, that is, the vehicle speed can be obtained more accurately when the vehicle accelerates greatly (starting, sudden braking, etc.).

附图说明BRIEF DESCRIPTION OF THE DRAWINGS

为了更清楚地说明本发明实施例中的技术方案,下面将对实施例描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

图1为本发明一种适用于多场景的自车速度估测方法之流程示意图;Figure 1 is a schematic flow chart of a self-vehicle speed estimation method suitable for multiple scenarios according to the present invention;

图2为本发明一种适用于多场景的自车速度估测方法实施例之自车估速处理流程示意图;Figure 2 is a schematic flowchart of the self-vehicle speed estimation process according to an embodiment of the self-vehicle speed estimation method suitable for multiple scenarios according to the present invention;

图3为本发明一种适用于多场景的自车速度估测方法实施例之实测快速路场景示意图;Figure 3 is a schematic diagram of an actual measured expressway scene according to an embodiment of the self-vehicle speed estimation method suitable for multiple scenarios according to the present invention;

图4为本发明一种适用于多场景的自车速度估测系统之架构示意图;Figure 4 is a schematic structural diagram of a self-vehicle speed estimation system suitable for multiple scenarios according to the present invention;

图5本发明一种适用于多场景的自车速度估测平台之架构示意图;Figure 5 is a schematic diagram of the architecture of a self-vehicle speed estimation platform suitable for multiple scenarios according to the present invention;

本发明目的实现、功能特点及优点将结合实施例,参照附图做进一步说明。The realization of the purpose, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings.

具体实施方式Detailed ways

为便于更好的理解本发明的目的、技术方案和优点更加清楚,下面结合附图和具体的实施方式对本发明作进一步说明,本领域技术人员可由本说明书所揭示的内容轻易地了解本发明的其它优点与功效。In order to better understand the purpose, technical solutions and advantages of the present invention more clearly, the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Those skilled in the art can easily understand the present invention from the contents disclosed in this specification. Other advantages and effects.

本发明亦可通过其它不同的具体实例加以施行或应用,本说明书中的各项细节亦可基于不同观点与应用,在不背离本发明的精神下进行各种修饰与变更。The present invention may also be implemented or applied through other different specific examples, and the details in this specification may also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

需要说明,若本发明实施例中有涉及方向性指示(诸如上、下、左、右、前、后……),则该方向性指示仅用于解释在某一特定姿态(如附图所示)下各部件之间的相对位置关系、运动情况等,如果该特定姿态发生改变时,则该方向性指示也相应地随之改变。It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back...), then the directional indications are only used to explain the position of a certain posture (as shown in the drawings). The relative positional relationship, movement conditions, etc. between the components under the display). If the specific posture changes, the directional indication will also change accordingly.

另外,若本发明实施例中有涉及“第一”、“第二”等的描述,则该“第一”、“第二”等的描述仅用于描述目的,而不能理解为指示或暗示其相对重要性或者隐含指明所指示的技术特征的数量。由此,限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。其次,各个实施例之间的技术方案可以相互结合,但是必须是以本领域普通技术人员能够实现为基础,当技术方案的结合出现相互矛盾或无法实现时,应当认为这种技术方案的结合不存在,也不在本发明要求的保护范围之内。In addition, if there are descriptions involving “first”, “second”, etc. in the embodiments of the present invention, the descriptions of “first”, “second”, etc. are only for descriptive purposes and shall not be understood as indications or implications. Its relative importance or implicit indication of the number of technical features indicated. Therefore, features defined as "first" and "second" may explicitly or implicitly include at least one of these features. Secondly, the technical solutions in various embodiments can be combined with each other, but it must be based on the realization by those of ordinary skill in the art. When the combination of technical solutions is contradictory or cannot be realized, it should be considered that the combination of technical solutions is not effective. exists and is not within the protection scope required by the present invention.

优选地,本发明一种适用于多场景的自车速度估测方法应用在一个或者多个终端或者服务器中。所述终端是一种能够按照事先设定或存储的指令,自动进行数值计算和/或信息处理的设备,其硬件包括但不限于微处理器、专用集成电路(Application SpecificIntegrated Circuit,ASIC)、可编程门阵列(Field-Programmable GateArray,FPGA)、数字处理器(Digital Signal Processor,DSP)、嵌入式设备等。Preferably, the self-vehicle speed estimation method suitable for multiple scenarios of the present invention is applied in one or more terminals or servers. The terminal is a device that can automatically perform numerical calculations and/or information processing according to preset or stored instructions. Its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Programmable gate array (Field-Programmable GateArray, FPGA), digital processor (Digital Signal Processor, DSP), embedded devices, etc.

所述终端可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述终端可以与客户通过键盘、鼠标、遥控器、触摸板或声控设备等方式进行人机交互。The terminal can be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal can interact with the client through a keyboard, a mouse, a remote control, a touch pad, or a voice control device.

本发明为实现一种适用于多场景的自车速度估测方法、系统及平台。The present invention is to realize a vehicle speed estimation method, system and platform applicable to multiple scenarios.

如图1所示,是本发明实施例提供的适用于多场景的自车速度估测方法的流程图。As shown in Figure 1, it is a flow chart of a self-vehicle speed estimation method suitable for multiple scenarios provided by an embodiment of the present invention.

在本实施例中,所述适用于多场景的自车速度估测方法,可以应用于具备显示功能的终端或者固定终端中,所述终端并不限定于个人电脑、智能手机、平板电脑、安装有摄像头的台式机或一体机等。In this embodiment, the self-vehicle speed estimation method suitable for multiple scenarios can be applied to terminals with display functions or fixed terminals. The terminals are not limited to personal computers, smart phones, tablets, installations, etc. Desktop or all-in-one computers with cameras, etc.

所述适用于多场景的自车速度估测方法也可以应用于由终端和通过网络与所述终端进行连接的服务器所构成的硬件环境中。网络包括但不限于:广域网、城域网或局域网。本发明实施例的适用于多场景的自车速度估测方法可以由服务器来执行,也可以由终端来执行,还可以是由服务器和终端共同执行。The self-vehicle speed estimation method suitable for multiple scenarios can also be applied to a hardware environment composed of a terminal and a server connected to the terminal through a network. Networks include, but are not limited to: wide area networks, metropolitan area networks, or local area networks. The self-vehicle speed estimation method suitable for multiple scenarios in the embodiment of the present invention can be executed by the server, the terminal, or both the server and the terminal.

例如,对于需要进行适用于多场景的自车速度估测终端,可以直接在终端上集成本发明的方法所提供的适用于多场景的自车速度估测功能,或者安装用于实现本发明的方法的客户端。再如,本发明所提供的方法还可以软件开发工具包(Software DevelopmentKit,SDK)的形式运行在服务器等设备上,以SDK的形式提供适用于多场景的自车速度估测功能的接口,终端或其他设备通过所提供的接口即可实现适用于多场景的自车速度估测功能。以下结合附图对本发明作进一步阐述。For example, for a terminal that needs to estimate the own vehicle speed suitable for multiple scenarios, the self-vehicle speed estimation function suitable for multiple scenarios provided by the method of the present invention can be directly integrated on the terminal, or a terminal for implementing the present invention can be installed. method client. For another example, the method provided by the present invention can also be run on a server or other equipment in the form of a software development kit (SDK), and an interface for the self-vehicle speed estimation function suitable for multiple scenarios is provided in the form of the SDK, and the terminal Or other devices can realize the self-vehicle speed estimation function suitable for multiple scenarios through the provided interface. The present invention will be further described below in conjunction with the accompanying drawings.

如图1所示,本发明提供了一种适用于多场景的自车速度估测方法,所述方法包括如下步骤:As shown in Figure 1, the present invention provides a self-vehicle speed estimation method suitable for multiple scenarios. The method includes the following steps:

S1、获取场景中与自车相对应检测点的第一数据,根据所述第一数据生成与第一数据相对应的自车纵向速度;其中,所述第一数据包括检测点距离数据、检测点速度数据和检测点角度数据;S1. Obtain the first data of the detection point corresponding to the self-vehicle in the scene, and generate the longitudinal speed of the self-vehicle corresponding to the first data according to the first data; wherein the first data includes detection point distance data, detection point distance data, and detection point distance data. Point speed data and detection point angle data;

S2、根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况;其中,所述第二数据包括自车车速数据、自车偏转角数据、自车纵向加速度数据和自车轮速数据;S2. According to the longitudinal speed of the self-vehicle, combined with the second data corresponding to the self-vehicle body, generate a stationary point velocity distribution corresponding to the self-vehicle body; wherein the second data includes the self-vehicle speed data, the self-vehicle deflection Angle data, vehicle longitudinal acceleration data and vehicle wheel speed data;

S3、根据所述静止点速度分布情况,实时生成与自车身相对应的自车速度数据。S3. According to the stationary point speed distribution, generate vehicle speed data corresponding to the vehicle body in real time.

所述自车纵向速度的计算公式如下所示:The calculation formula of the vehicle's longitudinal speed is as follows:

Votg=V/cosθ (1)V otg =V/cosθ (1)

其中,Votg纵为纵向速度;V为检测点的径向速度,θ为检测点的方位角。Among them, V otg is the longitudinal velocity; V is the radial velocity of the detection point, and θ is the azimuth angle of the detection point.

所述根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况,还包括:The step of generating a static point velocity distribution corresponding to the vehicle body according to the longitudinal velocity of the vehicle body in combination with the second data corresponding to the vehicle body further includes:

S21、创建与检测点动静属性相对应的第一门限阈值;其中,所述第一门限阈值为用于判断检测点动静属性门限;S21. Create a first threshold corresponding to the motion and static attributes of the detection point; wherein the first threshold is a threshold used to determine the motion and static attributes of the detection point;

S22、判定生成与所述自车纵向速度相对应且小于所述第一门限阈值的第一检测点数据。S22. Determine to generate first detection point data corresponding to the longitudinal speed of the own vehicle and smaller than the first threshold.

所述第一门限阈值的计算公式如下所示:The calculation formula of the first threshold value is as follows:

Threshold1=Verr+cosθ·lon+sinθ·lat (2)Threshold1=V err +cosθ·lon+sinθ·lat (2)

其中,Verr为最小基线误差,lon为纵向速度修正,lat为横向速度修正。Among them, V err is the minimum baseline error, lon is the longitudinal speed correction, and lat is the transverse speed correction.

所述根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况,还包括:Generating a stationary point velocity distribution corresponding to the self-body according to the longitudinal speed of the self-vehicle, combined with the second data corresponding to the self-body, further includes:

S23、根据自车场景情况,创建与检测点动静属性相对应的第二门限阈值;其中,所述第二门限阈值为满足第一门限阈值的有效点数量门限;S23. According to the situation of the vehicle scene, create a second threshold corresponding to the motion and static attributes of the detection point; wherein the second threshold is the threshold of the number of valid points that satisfies the first threshold;

S24、判定生成与所述自车纵向速度相对应且小于所述第二门限阈值的第二检测点数据。S24. Determine to generate second detection point data corresponding to the longitudinal speed of the own vehicle and smaller than the second threshold.

所述根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况,还包括:Generating a stationary point velocity distribution corresponding to the self-body according to the longitudinal speed of the self-vehicle, combined with the second data corresponding to the self-body, further includes:

S25、结合直方图统计,创建与直方图相对应的直方图参数;其中,所述直方图参数包括极差、组距和组数;S25. Combined with histogram statistics, create histogram parameters corresponding to the histogram; wherein the histogram parameters include range, group distance and number of groups;

S26、根据所述直方图参数,生成与检测点相对应的组别数据;其中,所述组别数据包括各个检测点所在组号;S26. Generate group data corresponding to the detection points according to the histogram parameters; wherein the group data includes the group number of each detection point;

S27、根据所述组别数据,依次比较各个组别中的检测点个数,并生成与检测点个数相对应的组号数据;其中,所述组号数据为检测点个数组所在的组号。S27. According to the group data, the number of detection points in each group is compared in turn, and group number data corresponding to the number of detection points is generated; wherein the group number data is the group number to which the detection point array is located.

所述根据所述静止点速度分布情况,实时生成与自车身相对应的自车速度数据,还包括:The method of generating the vehicle speed data corresponding to the vehicle body in real time according to the stationary point speed distribution also includes:

S31、结合线性滤波,对与自车身相对应的偏转角数据进行平滑处理;S31. Combine with linear filtering to smooth the deflection angle data corresponding to the self-body;

S32、创建与自车纵向加速度相对应的第三门限阈值,并根据所述第三门限阈值判定自车速度数据是否存在异常;其中,所述第三门限阈值为自车最大纵向加速度的30%。S32. Create a third threshold corresponding to the longitudinal acceleration of the own vehicle, and determine whether there is an abnormality in the speed data of the own vehicle based on the third threshold; wherein the third threshold is 30% of the maximum longitudinal acceleration of the own vehicle. .

具体地,在本发明实施例中,提供一种基于毫米波雷达的自车速度估计方法,该方法基于直方图统计和自车车身传感器输出的车速、轮速、加速度等信息,结合实际场景,自适应选择不同方法计算的估速结果,该方法适应性较好、计算简单,可以实时获取自车的准确速度。Specifically, in an embodiment of the present invention, a method for estimating the speed of a vehicle based on millimeter-wave radar is provided. The method is based on histogram statistics and information such as vehicle speed, wheel speed, acceleration, etc. output by the vehicle body sensor, and is combined with actual scenarios to adaptively select speed estimation results calculated by different methods. The method has good adaptability and simple calculation, and can obtain the accurate speed of the vehicle in real time.

为实现上述目的,本发明采用如下技术方案:In order to achieve the above objects, the present invention adopts the following technical solutions:

步骤1:获取当前帧所有检测点的RVA信息(距离、速度、角度),并计算各个检测点的纵向速度:Step 1: Obtain the RVA information (distance, speed, angle) of all detection points in the current frame, and calculate the longitudinal speed of each detection point:

Votg=V/cosθ (1)V otg =V/cosθ (1)

式中:V为检测点的径向速度,θ为检测点的方位角。In the formula: V is the radial velocity of the detection point, and θ is the azimuth angle of the detection point.

步骤2:从车身传感器中获取部分车身信息:自车车速Vveh、偏转角yawRate、自车纵向加速度longAcc、自车轮速wheelSpeed[4]。Step 2: Obtain part of the vehicle body information from the vehicle body sensors: vehicle speed V veh , deflection angle yawRate, vehicle longitudinal acceleration longAcc, vehicle wheel speed wheelSpeed[4].

步骤3:采用直方图统计绝对静止点的速度分布:Step 3: Use histogram statistics to calculate the velocity distribution of the absolute stationary point:

首先,选取有效数据,即纵向速度满足Votg<Threshold1的检测点,Threshold1为用于判断检测点动静属性的门限:First, select valid data, that is, the detection point whose longitudinal velocity satisfies V otg <Threshold1. Threshold1 is the threshold used to judge the dynamic and static attributes of the detection point:

Threshold1=Verr+cosθ·lon+sinθ·lat (2)Threshold1=V err +cosθ·lon+sinθ·lat (2)

式中:Verr为最小基线误差,lon为纵向速度修正,lat为横向速度修正,上述值均通过线下数据统计设置。In the formula: V err is the minimum baseline error, lon is the longitudinal speed correction, lat is the lateral speed correction, and the above values are all set through offline data statistics.

其次,设置直方图参数:极差、组距和组数。Secondly, set the histogram parameters: range, group distance and number of groups.

设置极差D:Set the range D:

D=Vmax-Vmin (3)D=V max -V min (3)

极差的设置只需考虑目标速度的范围,默认最大速度Vmax为50m/s,最小速度Vmin为0m/s。Extremely poor settings only need to consider the range of the target speed. The default maximum speed V max is 50m/s and the minimum speed V min is 0m/s.

设置组距d(d∈(0,D]):组距的设置需同时考虑速度结果精度和计算量,组距值越小,即直方图统计得到的最终速度越准确,相对应的计算量越大,可根据实际使用情况调整。Set the group distance d(d∈(0,D]): The setting of the group distance needs to consider both the speed result accuracy and the amount of calculation. The smaller the group distance value, that is, the more accurate the final speed obtained by histogram statistics, the corresponding calculation amount The larger it is, it can be adjusted according to actual usage.

设置组数:k=D/d;Set the number of groups: k=D/d;

再次,判断有效检测点数是否满足设置的阈值Threshold 2,该门限根据实际应用场景调整,考虑到个别场景静止目标较少,建议设置为1~10。若有效检测点数满足阈值Threshold 2,则继续处理,否则进入步骤4。Third, determine whether the number of effective detection points meets the set threshold Threshold 2. This threshold is adjusted according to the actual application scenario. Considering that there are few stationary targets in some scenes, it is recommended to set it to 1 to 10. If the number of valid detection points meets the threshold Threshold 2, continue processing, otherwise proceed to step 4.

再次,计算各个检测点所在的组号:Again, calculate the group number of each detection point:

index=Votg/d+(D-1)+0.5 (4)index=V otg /d+(D-1)+0.5 (4)

最后:统计落入各个组内的检测点个数,依次比较各个组的检测点个数,获取检测点个数最多的组所在的组号indexmax并计算估速:Finally: count the number of detection points that fall into each group, compare the number of detection points in each group in turn, get the group number index max of the group with the largest number of detection points and calculate the estimated speed:

Vest1=-(indexmax-(D-1))×d (5)V est1 =-(index max -(D-1))×d (5)

步骤4:使用线性滤波对偏转角yawRate进行平滑处理,使用如下公式:Step 4: Use linear filtering to smooth the deflection angle yawRate, using the following formula:

yawRate- k=yawRatek×K+yawRate- k-1×(1-K),...K∈[0,1] (6)yawRate - k =yawRate k ×K+yawRate - k-1 ×(1-K),...K∈[0,1] (6)

式中:yawRatek为当前帧从车身信息中获取的量测值,yawRate- k-1为上一帧的平滑值,yawRate-k为当前帧的平滑值,K为线性系数(K根据实际使用情况调整,使得平滑处理后的偏转角方差在一定范围内即可)。In the formula: yawRate k is the measurement value obtained from the body information of the current frame, yawRate - k-1 is the smooth value of the previous frame, yawRate - k is the smooth value of the current frame, K is the linear coefficient (K is based on actual use Adjust the situation so that the deflection angle variance after smoothing is within a certain range).

步骤5:判断轮速信息的有效性,即剔除异常轮速数据。若车速和轮速满足如下条件即认为轮速信息无效:Step 5: Determine the validity of wheel speed information, that is, eliminate abnormal wheel speed data. If the vehicle speed and wheel speed meet the following conditions, the wheel speed information is considered invalid:

式中:wheelSpeed[3]为左后轮速,wheelSpeed[4]为右后轮速,le-6为科学计数法,表示1乘10的负6次方,即0.000001。Where wheelSpeed[3] is the speed of the left rear wheel, wheelSpeed[4] is the speed of the right rear wheel, and le-6 is scientific notation, which means 1 times 10 to the negative sixth power, that is, 0.000001.

上述条件表示若自车处于运动状态且左后/右后轮速为0则认为轮速信息无效,此时进入步骤6;否则认为轮速信息有效,进入步骤7。The above conditions mean that if the vehicle is in motion and the left rear/right rear wheel speed is 0, the wheel speed information is considered invalid, and the process goes to step 6; otherwise, the wheel speed information is considered valid, and the process goes to step 7.

步骤6:计算最终的自车估速Vcomp。若步骤5中判断轮速信息无效,此时考虑两种情况:1)步骤3中有效检测点不满足阈值;2)Vest1和Vest2间存在较大差异,即Vest1-Vest2>10d。若上述两个条件任满足其一,则Vcomp=Vveh,否则Vcomp=Vest1Step 6: Calculate the final estimated speed V comp of the vehicle. If the wheel speed information is invalid in step 5, two situations are considered: 1) the effective detection point in step 3 does not meet the threshold; 2) there is a large difference between V est1 and V est2 , that is, V est1 -V est2 >10d. If either of the above two conditions is met, V comp =V veh , otherwise V comp =V est1 .

步骤7:若步骤5中判断轮速信息有效,进入该步骤:Step 7: If the wheel speed information is determined to be valid in step 5, enter this step:

首先,根据左后轮速和右后轮速计算车速Vest2First, calculate the vehicle speed V est2 based on the left rear wheel speed and the right rear wheel speed:

Vest2=(wheelSpeed[3]+wheelSpeed[4])/2 (8)V est2 =(wheelSpeed[3]+wheelSpeed[4])/2 (8)

其次:判断自车纵向加速度longAcc是否满足设置的阈值Threshold3,该门限需根据线下数据统计设置,一般设置为自车最大纵向加速度的30%。此时考虑三种情况:1)步骤3中有效检测点不满足阈值;2)Vest1和Vest2间存在较大差异,即Vest1-Vest2>5d;3)longAcc满足急刹条件(即longAcc大于最大纵向加速度的60%)。若纵向加速度满足阈值且上述三个条件任满足其一,则Vcomp=Vest2,否则Vcomp=Vest1Secondly: Determine whether the vehicle's longitudinal acceleration longAcc meets the set threshold Threshold3. This threshold needs to be set based on offline data statistics. It is generally set to 30% of the vehicle's maximum longitudinal acceleration. Consider three situations at this time: 1) The effective detection point in step 3 does not meet the threshold; 2) There is a large difference between V est1 and V est2 , that is, V est1 - V est2 >5d; 3) longAcc meets the emergency braking condition (i.e. longAcc is greater than 60% of the maximum longitudinal acceleration). If the longitudinal acceleration meets the threshold and any of the above three conditions is met, then V comp =V est2 , otherwise V comp =V est1 ;

最后:若longAcc不满足Threshold3,此时考虑两种情况:1)转弯场景,即yawRate较大(|yawRate|>ω,ω需根据实际应用场景调整);2)Vest1和Vest2间存在较大差异,即Vest1-Vest2>5d。若同时满足上述两个条件,则Vcomp=Vest1,否则Vcomp=Vest2Finally: If longAcc does not meet Threshold3, consider two situations: 1) turning scenario, that is, yawRate is large (|yawRate|>ω, ω needs to be adjusted according to the actual application scenario); 2) there is a gap between V est1 and V est2 Large difference, that is, V est1 -V est2 >5d. If the above two conditions are met at the same time, then V comp =V est1 , otherwise V comp =V est2 .

下面结合图3快速路场景实例说明本发明方案的处理流程,具体步骤如下:The processing flow of the solution of the present invention will be described below with reference to the example of the expressway scene in Figure 3. The specific steps are as follows:

步骤1:选取一帧数据,共有160个检测点,计算各个检测点的纵向速度。Step 1: Select a frame of data, with a total of 160 detection points, and calculate the longitudinal speed of each detection point.

步骤2:获取车身信息:Step 2: Get body information:

步骤3:采用直方图统计绝对静止点的速度分布:Step 3: Use a histogram to calculate the velocity distribution of the absolute stationary point:

首先,计算Threshold1,根据如下公式:First, calculate Threshold1 according to the following formula:

Threshold1=Verr+cosθ·lon+sinθ·lat (2)Threshold1=V err +cosθ·lon+sinθ·lat (2)

其中Verr=0.1,lon=0.95,lat=1.37,结合每个检测点的方位角可计算出各个检测点对应的动静门限,统计可得有效检测点数为146个。Among them, Verr = 0.1, lon = 0.95, and lat = 1.37. Combining the azimuth angle of each detection point, the dynamic and static thresholds corresponding to each detection point can be calculated. Statistics show that the number of effective detection points is 146.

其次,设置直方图参数:极差D=Vmax-Vmin=50-0=50、组距d=0.25和组数k=D/d=50/0.25=200。Secondly, the histogram parameters are set: range D=V max -V min =50-0=50, group distance d=0.25 and number of groups k=D/d=50/0.25=200.

再次,阈值Threshold2设置为5,有效检测点数满足阈值Threshold2。Again, the threshold Threshold2 is set to 5, and the number of valid detection points meets the threshold Threshold2.

最后,计算各个检测点所在的组号并统计落入各个组内的检测点个数,依次比较各个组的检测点个数,获取检测点个数最多的组所在的组号indexmax=105,计算估速:Vest1=-(indexmax-(D-1))×d=23.50。Finally, calculate the group number of each detection point and count the number of detection points falling into each group, compare the number of detection points in each group in turn, and obtain the group number of the group with the largest number of detection points, index max = 105, Calculate the estimated speed: V est1 =-(index max -(D-1))×d=23.50.

步骤4:按平滑滤波公式计算当前帧的yawRate=0.00175。Step 4: Calculate the yawRate=0.00175 of the current frame according to the smoothing filter formula.

步骤5:判断轮速信息有效,进入步骤7。Step 5: Determine whether the wheel speed information is valid and proceed to step 7.

步骤6:略过。Step 6: Skip.

步骤7:计算最终的自车估速VcompStep 7: Calculate the final estimated vehicle speed V comp .

首先,根据左后轮速和右后轮速计算车速(需转换为m/s):First, calculate the vehicle speed based on the left rear wheel speed and the right rear wheel speed (need to be converted to m/s):

Vest2=(wheelSpeed[3]+wheelSpeed[4])/2=(84.59+84.69)/2/3.6=23.51。V est2 =(wheelSpeed[3]+wheelSpeed[4])/2=(84.59+84.69)/2/3.6=23.51.

其次,纵向加速度阈值Threshold3设置为3m/s2,且longAcc不满足阈值。Secondly, the longitudinal acceleration threshold Threshold3 is set to 3m/s 2 , and longAcc does not meet the threshold.

最后,如下两种情况:1)转弯场景,即yawRate较大(|yawRate|>ω,ω需根据实际应用场景调整);2)Vest1和Vest2间存在较大差异,即Vest1-Vest2>5d,未能同时满足,故Vcomp=Vest2=23.51m/s。Finally, there are two situations: 1) turning scene, that is, yawRate is large (|yawRate|>ω, ω needs to be adjusted according to the actual application scenario); 2) there is a big difference between V est1 and V est2 , that is, V est1 -V est2 >5d, which cannot be satisfied at the same time, so V comp =V est2 =23.51m/s.

为实现上述目的,本发明还提供一种适用于多场景的自车速度估测系统,如图4所示,所述系统应用于所述的自车速度估测方法;所述系统包括:In order to achieve the above object, the present invention also provides a self-vehicle speed estimation system suitable for multiple scenarios. As shown in Figure 4, the system is applied to the self-vehicle speed estimation method; the system includes:

获取生成单元,用于获取场景中与自车相对应检测点的第一数据,根据所述第一数据生成与第一数据相对应的自车纵向速度;其中,所述第一数据包括检测点距离数据、检测点速度数据和检测点角度数据;The acquisition and generation unit is used to obtain the first data of the detection point corresponding to the self-vehicle in the scene, and generate the longitudinal speed of the self-vehicle corresponding to the first data according to the first data; wherein the first data includes the detection point Distance data, detection point speed data and detection point angle data;

第一生成单元,用于根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况;其中,所述第二数据包括自车车速数据、自车偏转角数据、自车纵向加速度数据和自车轮速数据;A first generation unit configured to generate a stationary point velocity distribution corresponding to the self-vehicle body based on the longitudinal speed of the self-vehicle in combination with second data corresponding to the self-vehicle body; wherein the second data includes the self-vehicle speed Data, vehicle deflection angle data, vehicle longitudinal acceleration data and vehicle wheel speed data;

第二生成单元,用于根据所述静止点速度分布情况,实时生成与自车身相对应的自车速度数据。The second generation unit is used to generate the vehicle speed data corresponding to the vehicle body in real time according to the stationary point speed distribution.

进一步地,所述自车纵向速度的计算公式如下所示:Further, the calculation formula of the vehicle's longitudinal speed is as follows:

Votg=V/cosθ (1)V otg =V/cosθ (1)

其中,Votg纵为纵向速度;V为检测点的径向速度,θ为检测点的方位角;Among them, V otg is the longitudinal velocity; V is the radial velocity of the detection point, and θ is the azimuth angle of the detection point;

所述第一生成单元,还包括:The first generation unit also includes:

第一创建模块,用于创建与检测点动静属性相对应的第一门限阈值;其中,所述第一门限阈值为用于判断检测点动静属性门限;The first creation module is used to create a first threshold corresponding to the motion and static attributes of the detection point; wherein the first threshold is a threshold for judging the motion and static attributes of the detection point;

第一判定模块,用于判定生成与所述自车纵向速度相对应且小于所述第一门限阈值的第一检测点数据;A first determination module, configured to determine and generate first detection point data corresponding to the longitudinal speed of the own vehicle and less than the first threshold;

所述第一门限阈值的计算公式如下所示:The calculation formula of the first threshold value is as follows:

Threshold1=Verr+cosθ·lon+sinθ·lat (2)Threshold1=V err +cosθ·lon+sinθ·lat (2)

其中,Verr为最小基线误差,lon为纵向速度修正,lat为横向速度修正;Among them, V err is the minimum baseline error, lon is the longitudinal velocity correction, and lat is the transverse velocity correction;

和/或,所述第一生成单元,还包括:And/or, the first generating unit further includes:

第二创建模块,用于根据自车场景情况,创建与检测点动静属性相对应的第二门限阈值;其中,所述第二门限阈值为满足第一门限阈值的有效点数量门限;The second creation module is used to create a second threshold corresponding to the motion and static attributes of the detection point according to the situation of the self-vehicle scene; wherein the second threshold is a threshold of the number of valid points that satisfies the first threshold;

第二判定模块,用于判定生成与所述自车纵向速度相对应且小于所述第二门限阈值的第二检测点数据;A second determination module, used for determining and generating second detection point data corresponding to the longitudinal speed of the vehicle and less than the second threshold value;

和/或,所述第一生成单元,还包括:And/or, the first generation unit also includes:

第三创建模块,用于结合直方图统计,创建与直方图相对应的直方图参数;其中,所述直方图参数包括极差、组距和组数;The third creation module is used to combine histogram statistics and create histogram parameters corresponding to the histogram; wherein the histogram parameters include range, group distance and number of groups;

第一生成模块,用于根据所述直方图参数,生成与检测点相对应的组别数据;其中,所述组别数据包括各个检测点所在组号;The first generation module is used to generate group data corresponding to the detection points according to the histogram parameters; wherein the group data includes the group number of each detection point;

第二生成模块,用于根据所述组别数据,依次比较各个组别中的检测点个数,并生成与检测点个数相对应的组号数据;其中,所述组号数据为检测点个数组所在的组号;The second generation module is used to sequentially compare the number of detection points in each group according to the group data, and generate group number data corresponding to the number of detection points; wherein the group number data is the detection point The group number where the array is located;

和/或,所述第二生成单元,还包括:And/or, the second generating unit further includes:

第一处理模块,用于结合线性滤波,对与自车身相对应的偏转角数据进行平滑处理;The first processing module is used to combine linear filtering to smooth the deflection angle data corresponding to the self-body;

第三判定模块,用于创建与自车纵向加速度相对应的第三门限阈值,并根据所述第三门限阈值判定自车速度数据是否存在异常;其中,所述第三门限阈值为自车最大纵向加速度的30%。The third determination module is used to create a third threshold corresponding to the longitudinal acceleration of the own vehicle, and determine whether there is an abnormality in the speed data of the own vehicle according to the third threshold; wherein the third threshold is the maximum value of the own vehicle. 30% of longitudinal acceleration.

在本发明系统方案实施例中,所述的一种适用于多场景的自车速度估测系统中涉及的方法步骤,具体细节已在上文阐述,也就是说,所述系统中的功能模块用于实现上述方法实施例中的步骤或子步骤,此处不再赘述。In the system solution embodiment of the present invention, the specific details of the method steps involved in the self-vehicle speed estimation system suitable for multiple scenarios have been explained above, that is to say, the functional modules in the system The steps or sub-steps used to implement the above method embodiments will not be described again here.

为实现上述目的,本发明还提供一种适用于多场景的自车速度估测平台,如图5所示,包括处理器、存储器以及适用于多场景的自车速度估测平台控制程序;其中,在所述的处理器执行所述的适用于多场景的自车速度估测平台控制程序,所述的适用于多场景的自车速度估测平台控制程序被存储在所述存储器中,所述的适用于多场景的自车速度估测平台控制程序,实现所述的适用于多场景的自车速度估测方法步骤。例如:In order to achieve the above object, the present invention also provides a self-vehicle speed estimation platform suitable for multiple scenarios, as shown in Figure 5, including a processor, a memory and a self-vehicle speed estimation platform control program suitable for multiple scenarios; wherein , when the processor executes the self-vehicle speed estimation platform control program suitable for multiple scenarios, the self-vehicle speed estimation platform control program suitable for multiple scenarios is stored in the memory, so The above-described self-vehicle speed estimation platform control program suitable for multiple scenarios implements the steps of the described self-vehicle speed estimation method suitable for multiple scenarios. For example:

S1、获取场景中与自车相对应检测点的第一数据,根据所述第一数据生成与第一数据相对应的自车纵向速度;其中,所述第一数据包括检测点距离数据、检测点速度数据和检测点角度数据;S1. Obtain the first data of the detection point corresponding to the self-vehicle in the scene, and generate the longitudinal speed of the self-vehicle corresponding to the first data according to the first data; wherein the first data includes detection point distance data, detection point distance data, and detection point distance data. Point speed data and detection point angle data;

S2、根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况;其中,所述第二数据包括自车车速数据、自车偏转角数据、自车纵向加速度数据和自车轮速数据;S2. According to the longitudinal speed of the self-vehicle, combined with the second data corresponding to the self-vehicle body, generate a stationary point velocity distribution corresponding to the self-vehicle body; wherein the second data includes the self-vehicle speed data, the self-vehicle deflection Angle data, vehicle longitudinal acceleration data and vehicle wheel speed data;

S3、根据所述静止点速度分布情况,实时生成与自车身相对应的自车速度数据。S3. According to the stationary point speed distribution, generate vehicle speed data corresponding to the vehicle body in real time.

步骤具体细节已在上文阐述,此处不再赘述。The specific details of the steps have been explained above and will not be repeated here.

本发明实施例中,所述的适用于多场景的自车速度估测平台内置处理器,可以由集成电路组成,例如可以由单个封装的集成电路所组成,也可以是由多个相同功能或不同功能封装的集成电路所组成,包括一个或者多个中央处理器(Central Processing unit,CPU)、微处理器、数字处理芯片、图形处理器及各种控制芯片的组合等。处理器利用各种接口和线路连接取各个部件,通过运行或执行存储在存储器内的程序或者单元,以及调用存储在存储器内的数据,以执行适用于多场景的自车速度估测各种功能和处理数据;In the embodiment of the present invention, the built-in processor of the self-vehicle speed estimation platform suitable for multiple scenarios can be composed of integrated circuits, for example, it can be composed of a single packaged integrated circuit, or it can be composed of multiple identical functions or It is composed of integrated circuits with different functional packages, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors and a combination of various control chips. The processor uses various interfaces and lines to connect various components, runs or executes programs or units stored in the memory, and calls data stored in the memory to perform various functions of self-vehicle speed estimation suitable for multiple scenarios. and process data;

存储器用于存储程序代码和各种数据,安装在适用于多场景的自车速度估测平台中,并在运行过程中实现高速、自动地完成程序或数据的存取。The memory is used to store program codes and various data. It is installed in the self-vehicle speed estimation platform suitable for multiple scenarios, and realizes high-speed and automatic access to programs or data during operation.

所述存储器包括只读存储器(Read-Only Memory,ROM),随机存储器(RandomAccess Memory,RAM)、可编程只读存储器(Programmable Read-Only Memory,PROM)、可擦除可编程只读存储器(Erasable Programmable Read-Only Memory,EPROM)、一次可编程只读存储器(One-time Programmable Read-Only Memory,OTPROM)、电子擦除式可复写只读存储器(Electrically-Erasable Programmable Read-Only Memory,EEPROM)、只读光盘(Compact Disc Read-Only Memory,CD-ROM)或其他光盘存储器、磁盘存储器、磁带存储器、或者能够用于携带或存储数据的计算机可读的任何其他介质。The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electronically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

本发明通过获取场景中与自车相对应检测点的第一数据,根据所述第一数据生成与第一数据相对应的自车纵向速度;其中,所述第一数据包括检测点距离数据、检测点速度数据和检测点角度数据;根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况;其中,所述第二数据包括自车车速数据、自车偏转角数据、自车纵向加速度数据和自车轮速数据;根据所述静止点速度分布情况,实时生成与自车身相对应的自车速度数据,以及和方法相对应的系统、平台,以实现实时获取自车的准确速度的效果,即可以在自车加速度较大的情况下(起步、急刹等)获取更加准确的车速。The present invention obtains the first data of the detection point corresponding to the self-vehicle in the scene, and generates the longitudinal speed of the self-vehicle corresponding to the first data according to the first data; wherein the first data includes detection point distance data, Detection point speed data and detection point angle data; according to the longitudinal speed of the own vehicle, combined with the second data corresponding to the own vehicle body, a stationary point velocity distribution corresponding to the own vehicle body is generated; wherein the second data includes Self-vehicle speed data, self-vehicle deflection angle data, self-vehicle longitudinal acceleration data and self-vehicle wheel speed data; according to the stationary point speed distribution, self-vehicle speed data corresponding to the self-vehicle body is generated in real time, as well as the self-vehicle speed data corresponding to the method. The system and platform can achieve the effect of obtaining the accurate speed of the own vehicle in real time, that is, the vehicle speed can be obtained more accurately when the vehicle accelerates greatly (starting, sudden braking, etc.).

以上所述实施例仅表达了本发明的几种实施方式,其描述较为具体和详细,但并不能因此而理解为对本发明专利范围的限制。应当指出的是,对于本领域的普通技术人员来说,在不脱离本发明构思的前提下,还可以做出若干变形和改进,这些都属于本发明的保护范围。因此,本发明专利的保护范围应以所附权利要求为准。The above-mentioned embodiments only express several implementation modes of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of the present invention. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the scope of protection of the patent of the present invention should be determined by the appended claims.

Claims (10)

1.一种适用于多场景的自车速度估测方法,其特征在于,所述方法包括如下步骤:1. A self-vehicle speed estimation method suitable for multiple scenarios, characterized in that the method includes the following steps: 获取场景中与自车相对应检测点的第一数据,根据所述第一数据生成与第一数据相对应的自车纵向速度;其中,所述第一数据包括检测点距离数据、检测点速度数据和检测点角度数据;Obtain the first data of the detection point corresponding to the self-vehicle in the scene, and generate the longitudinal speed of the self-vehicle corresponding to the first data according to the first data; wherein the first data includes detection point distance data, detection point speed Data and detection point angle data; 根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况;其中,所述第二数据包括自车车速数据、自车偏转角数据、自车纵向加速度数据和自车轮速数据;According to the longitudinal speed of the self-vehicle, combined with the second data corresponding to the self-vehicle body, a stationary point velocity distribution corresponding to the self-vehicle body is generated; wherein the second data includes the self-vehicle speed data and the self-vehicle deflection angle data. , vehicle longitudinal acceleration data and vehicle wheel speed data; 根据所述静止点速度分布情况,实时生成与自车身相对应的自车速度数据。According to the stationary point speed distribution, the vehicle speed data corresponding to the vehicle body is generated in real time. 2.根据权利要求1所述的一种适用于多场景的自车速度估测方法,其特征在于,所述自车纵向速度的计算公式如下所示:2. A self-vehicle speed estimation method suitable for multiple scenarios according to claim 1, characterized in that the calculation formula of the self-vehicle longitudinal speed is as follows: Votg=V/cosθ (1)V otg =V/cosθ (1) 其中,Votg纵为纵向速度;V为检测点的径向速度,θ为检测点的方位角。Among them, V otg is the longitudinal velocity; V is the radial velocity of the detection point, and θ is the azimuth angle of the detection point. 3.根据权利要求1所述的一种适用于多场景的自车速度估测方法,其特征在于,所述根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况,还包括:3. A self-vehicle speed estimation method suitable for multiple scenarios according to claim 1, characterized in that, according to the longitudinal speed of the self-vehicle, combined with the second data corresponding to the self-vehicle body, a The static point velocity distribution corresponding to the self-body also includes: 创建与检测点动静属性相对应的第一门限阈值;其中,所述第一门限阈值为用于判断检测点动静属性门限;Create a first threshold corresponding to the motion and static attributes of the detection point; wherein the first threshold is a threshold for judging the motion and static attributes of the detection point; 判定生成与所述自车纵向速度相对应且小于所述第一门限阈值的第一检测点数据。It is determined to generate first detection point data corresponding to the longitudinal speed of the own vehicle and smaller than the first threshold. 4.根据权利要求3所述的一种适用于多场景的自车速度估测方法,其特征在于,所述第一门限阈值的计算公式如下所示:4. A self-vehicle speed estimation method suitable for multiple scenarios according to claim 3, characterized in that the calculation formula of the first threshold is as follows: Threshold1=Verr+cosθ·lon+sinθ·lat (2)Threshold1=V err +cosθ·lon+sinθ·lat (2) 其中,Verr为最小基线误差,lon为纵向速度修正,lat为横向速度修正。Among them, V err is the minimum baseline error, lon is the longitudinal speed correction, and lat is the transverse speed correction. 5.根据权利要求1或3所述的一种适用于多场景的自车速度估测方法,其特征在于,所述根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况,还包括:5. The method for estimating the vehicle speed applicable to multiple scenarios according to claim 1 or 3, characterized in that the step of generating the static point speed distribution corresponding to the vehicle body according to the longitudinal speed of the vehicle and the second data corresponding to the vehicle body further comprises: 根据自车场景情况,创建与检测点动静属性相对应的第二门限阈值;其中,所述第二门限阈值为满足第一门限阈值的有效点数量门限;According to the vehicle scene situation, create a second threshold corresponding to the motion and static attributes of the detection point; wherein the second threshold is the threshold of the number of valid points that satisfies the first threshold; 判定生成与所述自车纵向速度相对应且小于所述第二门限阈值的第二检测点数据。It is determined to generate second detection point data corresponding to the longitudinal speed of the own vehicle and smaller than the second threshold value. 6.根据权利要求5所述的一种适用于多场景的自车速度估测方法,其特征在于,所述根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况,还包括:6. A self-vehicle speed estimation method suitable for multiple scenarios according to claim 5, characterized in that, according to the longitudinal speed of the self-vehicle, combined with the second data corresponding to the self-vehicle body, a The static point velocity distribution corresponding to the self-body also includes: 结合直方图统计,创建与直方图相对应的直方图参数;其中,所述直方图参数包括极差、组距和组数;In combination with histogram statistics, histogram parameters corresponding to the histogram are created; wherein the histogram parameters include range, group interval and number of groups; 根据所述直方图参数,生成与检测点相对应的组别数据;其中,所述组别数据包括各个检测点所在组号;According to the histogram parameters, group data corresponding to the detection points are generated; wherein the group data includes the group number where each detection point is located; 根据所述组别数据,依次比较各个组别中的检测点个数,并生成与检测点个数相对应的组号数据;其中,所述组号数据为检测点个数组所在的组号。According to the group data, the number of detection points in each group is sequentially compared, and group number data corresponding to the number of detection points is generated; wherein the group number data is the group number where the array of detection points is located. 7.根据权利要求1所述的一种适用于多场景的自车速度估测方法,其特征在于,所述根据所述静止点速度分布情况,实时生成与自车身相对应的自车速度数据,还包括:7. A self-vehicle speed estimation method suitable for multiple scenarios according to claim 1, characterized in that the self-vehicle speed data corresponding to the self-vehicle body is generated in real time according to the stationary point speed distribution. ,Also includes: 结合线性滤波,对与自车身相对应的偏转角数据进行平滑处理;Combined with linear filtering, the deflection angle data corresponding to the self-body body is smoothed; 创建与自车纵向加速度相对应的第三门限阈值,并根据所述第三门限阈值判定自车速度数据是否存在异常;其中,所述第三门限阈值为自车最大纵向加速度的30%。Create a third threshold corresponding to the longitudinal acceleration of the own vehicle, and determine whether there is an abnormality in the speed data of the own vehicle based on the third threshold; wherein the third threshold is 30% of the maximum longitudinal acceleration of the own vehicle. 8.一种适用于多场景的自车速度估测系统,其特征在于,所述系统应用于如权利要求1-7任一项所述的自车速度估测方法;所述系统包括:8. A self-vehicle speed estimation system suitable for multiple scenarios, characterized in that the system is applied to the self-vehicle speed estimation method according to any one of claims 1-7; the system includes: 获取生成单元,用于获取场景中与自车相对应检测点的第一数据,根据所述第一数据生成与第一数据相对应的自车纵向速度;其中,所述第一数据包括检测点距离数据、检测点速度数据和检测点角度数据;The acquisition and generation unit is used to obtain the first data of the detection point corresponding to the self-vehicle in the scene, and generate the longitudinal speed of the self-vehicle corresponding to the first data according to the first data; wherein the first data includes the detection point Distance data, detection point speed data and detection point angle data; 第一生成单元,用于根据所述自车纵向速度,结合与自车身相对应的第二数据,生成与自车身相对应的静止点速度分布情况;其中,所述第二数据包括自车车速数据、自车偏转角数据、自车纵向加速度数据和自车轮速数据;A first generation unit configured to generate a stationary point velocity distribution corresponding to the self-vehicle body based on the longitudinal speed of the self-vehicle in combination with second data corresponding to the self-vehicle body; wherein the second data includes the self-vehicle speed Data, vehicle deflection angle data, vehicle longitudinal acceleration data and vehicle wheel speed data; 第二生成单元,用于根据所述静止点速度分布情况,实时生成与自车身相对应的自车速度数据。The second generating unit is used to generate the vehicle speed data corresponding to the vehicle body in real time according to the static point speed distribution. 9.根据权利要求8所述的一种适用于多场景的自车速度估测系统,其特征在于,所述自车纵向速度的计算公式如下所示:9. A self-vehicle speed estimation system suitable for multiple scenarios according to claim 8, characterized in that the calculation formula of the self-vehicle longitudinal speed is as follows: Votg=V/cosθ (1)V otg =V/cosθ (1) 其中,Votg纵为纵向速度;V为检测点的径向速度,θ为检测点的方位角;Among them, V otg is the longitudinal velocity; V is the radial velocity of the detection point, and θ is the azimuth angle of the detection point; 所述第一生成单元,还包括:The first generation unit also includes: 第一创建模块,用于创建与检测点动静属性相对应的第一门限阈值;其中,所述第一门限阈值为用于判断检测点动静属性门限;The first creation module is used to create a first threshold corresponding to the motion and static attributes of the detection point; wherein the first threshold is a threshold for judging the motion and static attributes of the detection point; 第一判定模块,用于判定生成与所述自车纵向速度相对应且小于所述第一门限阈值的第一检测点数据;A first determination module, configured to determine and generate first detection point data corresponding to the longitudinal speed of the own vehicle and less than the first threshold; 所述第一门限阈值的计算公式如下所示:The calculation formula of the first threshold value is as follows: Threshold1=Verr+cosθ·lon+sinθ·lat (2)Threshold1=V err +cosθ·lon+sinθ·lat (2) 其中,Verr为最小基线误差,lon为纵向速度修正,lat为横向速度修正;Among them, V err is the minimum baseline error, lon is the longitudinal velocity correction, and lat is the transverse velocity correction; 和/或,所述第一生成单元,还包括:And/or, the first generating unit further includes: 第二创建模块,用于根据自车场景情况,创建与检测点动静属性相对应的第二门限阈值;其中,所述第二门限阈值为满足第一门限阈值的有效点数量门限;The second creation module is used to create a second threshold corresponding to the motion and static attributes of the detection point according to the situation of the self-vehicle scene; wherein the second threshold is a threshold of the number of valid points that satisfies the first threshold; 第二判定模块,用于判定生成与所述自车纵向速度相对应且小于所述第二门限阈值的第二检测点数据;A second determination module, used for determining and generating second detection point data corresponding to the longitudinal speed of the vehicle and less than the second threshold value; 和/或,所述第一生成单元,还包括:And/or, the first generation unit also includes: 第三创建模块,用于结合直方图统计,创建与直方图相对应的直方图参数;其中,所述直方图参数包括极差、组距和组数;The third creation module is used to combine histogram statistics and create histogram parameters corresponding to the histogram; wherein the histogram parameters include range, group distance and number of groups; 第一生成模块,用于根据所述直方图参数,生成与检测点相对应的组别数据;其中,所述组别数据包括各个检测点所在组号;The first generation module is used to generate group data corresponding to the detection points according to the histogram parameters; wherein the group data includes the group number of each detection point; 第二生成模块,用于根据所述组别数据,依次比较各个组别中的检测点个数,并生成与检测点个数相对应的组号数据;其中,所述组号数据为检测点个数组所在的组号;The second generating module is used to compare the number of detection points in each group in turn according to the group data, and generate group number data corresponding to the number of detection points; wherein the group number data is the group number of the detection point array; 和/或,所述第二生成单元,还包括:And/or, the second generation unit also includes: 第一处理模块,用于结合线性滤波,对与自车身相对应的偏转角数据进行平滑处理;The first processing module is used to combine linear filtering to smooth the deflection angle data corresponding to the self-body; 第三判定模块,用于创建与自车纵向加速度相对应的第三门限阈值,并根据所述第三门限阈值判定自车速度数据是否存在异常;其中,所述第三门限阈值为自车最大纵向加速度的30%。The third determination module is used to create a third threshold corresponding to the longitudinal acceleration of the own vehicle, and determine whether there is an abnormality in the speed data of the own vehicle according to the third threshold; wherein the third threshold is the maximum value of the own vehicle. 30% of longitudinal acceleration. 10.一种适用于多场景的自车速度估测平台,其特征在于,包括处理器、存储器以及适用于多场景的自车速度估测平台控制程序;其中,在所述的处理器执行所述的适用于多场景的自车速度估测平台控制程序,所述的适用于多场景的自车速度估测平台控制程序被存储在所述存储器中,所述的适用于多场景的自车速度估测平台控制程序,实现如权利要求1至7中任一项所述的适用于多场景的自车速度估测方法。10. A self-vehicle speed estimation platform suitable for multiple scenarios, characterized in that it includes a processor, a memory, and a self-vehicle speed estimation platform control program suitable for multiple scenarios; wherein, when the processor executes The self-vehicle speed estimation platform control program suitable for multiple scenarios is stored in the memory, and the self-vehicle speed estimation platform control program suitable for multiple scenarios is stored in the memory. The speed estimation platform control program implements the self-vehicle speed estimation method suitable for multiple scenarios as described in any one of claims 1 to 7.
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