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

Abstract

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

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

Technical field

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.

Background technique

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.

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.

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.

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.

Summary of the invention

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.).

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.

The first purpose of the present invention is achieved in this way:

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;

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;

According to the static point speed distribution, the vehicle speed data corresponding to the vehicle body is generated in real time.

Further, the calculation formula of the vehicle's longitudinal speed is as follows:

V _otg =V/cosθ (1)

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.

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:

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;

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.

Further, the calculation formula of the first threshold is as follows:

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

Among them, V _err is the minimum baseline error, lon is the longitudinal speed correction, and lat is the transverse speed correction.

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:

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;

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.

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:

Combined with histogram statistics, create histogram parameters corresponding to the histogram; wherein the histogram parameters include range, group distance and number of groups;

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;

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.

Further, generating self-vehicle speed data corresponding to the self-vehicle body in real time based on the stationary point speed distribution also includes:

Combined with linear filtering, the deflection angle data corresponding to the self-body body is smoothed;

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.

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:

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;

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;

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.

Further, the calculation formula of the vehicle's longitudinal speed is as follows:

V _otg =V/cosθ (1)

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;

The first generation unit also includes:

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;

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;

The calculation formula of the first threshold value is as follows:

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

Among them, V _err is the minimum baseline error, lon is the longitudinal velocity correction, and lat is the transverse velocity correction;

And/or, the first generation unit also includes:

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;

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;

And/or, the first generation unit also includes:

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;

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;

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;

And/or, the second generation unit also includes:

The first processing module is used to combine linear filtering to smooth the deflection angle data corresponding to the self-body;

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.

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.

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.).

BRIEF DESCRIPTION OF THE DRAWINGS

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.

Figure 1 is a schematic flow chart of a self-vehicle speed estimation method suitable for multiple scenarios according to the present invention;

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;

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;

Figure 4 is a schematic structural diagram of a self-vehicle speed estimation system suitable for multiple scenarios according to the present invention;

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;

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.

Detailed ways

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.

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.

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.

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.

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.

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.

The present invention is to realize a vehicle speed estimation method, system and platform applicable to multiple scenarios.

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.

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.

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.

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.

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. 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. 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. According to the stationary point speed distribution, generate vehicle speed data corresponding to the vehicle body in real time.

The calculation formula of the vehicle's longitudinal speed is as follows:

V _otg =V/cosθ (1)

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.

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. 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. Determine to generate first detection point data corresponding to the longitudinal speed of the own vehicle and smaller than the first threshold.

The calculation formula of the first threshold value is as follows:

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

Among them, V _err is the minimum baseline error, lon is the longitudinal speed correction, and lat is the transverse speed correction.

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. 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. Determine to generate second detection point data corresponding to the longitudinal speed of the own vehicle and smaller than the second threshold.

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. Combined with histogram statistics, create histogram parameters corresponding to the histogram; wherein the histogram parameters include range, group distance and number of groups;

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. 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.

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. Combine with linear filtering to smooth the deflection angle data corresponding to the self-body;

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. .

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.

In order to achieve the above objects, the present invention adopts the following technical solutions:

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:

V _otg =V/cosθ (1)

In the formula: V is the radial velocity of the detection point, and θ is the azimuth angle of the detection point.

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].

Step 3: Use histogram statistics to calculate the velocity distribution of the absolute stationary point:

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＝V _err +cosθ·lon+sinθ·lat (2)

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.

Secondly, set the histogram parameters: range, group distance and number of groups.

Set the range D:

D＝V _max -V _min (3)

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.

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.

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

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.

Again, calculate the group number of each detection point:

index＝V _otg /d+(D-1)+0.5 (4)

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:

V _est1 =-(index _max -(D-1))×d (5)

Step 4: Use linear filtering to smooth the deflection angle yawRate, using the following formula:

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

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).

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:

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.

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.

Step 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 .

Step 7: If the wheel speed information is determined to be valid in step 5, enter this step:

First, calculate the vehicle speed V _est2 based on the left rear wheel speed and the right rear wheel speed:

V _est2 =(wheelSpeed[3]+wheelSpeed[4])/2 (8)

Secondly: 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 ;

Finally: 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 .

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:

Step 1: Select a frame of data, with a total of 160 detection points, and calculate the longitudinal speed of each detection point.

Step 2: Get body information:

Step 3: Use a histogram to calculate the velocity distribution of the absolute stationary point:

First, calculate Threshold1 according to the following formula:

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

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.

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.

Again, the threshold Threshold2 is set to 5, and the number of valid detection points meets the threshold Threshold2.

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.

Step 4: Calculate the yawRate=0.00175 of the current frame according to the smoothing filter formula.

Step 5: Determine whether the wheel speed information is valid and proceed to step 7.

Step 6: Skip.

Step 7: Calculate the final estimated vehicle speed V _comp .

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):

V _est2 =(wheelSpeed[3]+wheelSpeed[4])/2=(84.59+84.69)/2/3.6=23.51.

Secondly, the longitudinal acceleration threshold Threshold3 is set to 3m/s ² , and longAcc does not meet the threshold.

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.

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:

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;

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;

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.

Further, the calculation formula of the vehicle's longitudinal speed is as follows:

V _otg =V/cosθ (1)

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;

The first generation unit also includes:

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;

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;

The calculation formula of the first threshold value is as follows:

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

Among them, V _err is the minimum baseline error, lon is the longitudinal velocity correction, and lat is the transverse velocity correction;

And/or, the first generating unit further includes:

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;

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;

And/or, the first generation unit also includes:

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;

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;

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;

And/or, the second generating unit further includes:

The first processing module is used to combine linear filtering to smooth the deflection angle data corresponding to the self-body;

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.

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.

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. 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. 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. According to the stationary point speed distribution, generate vehicle speed data corresponding to the vehicle body in real time.

The specific details of the steps have been explained above and will not be repeated here.

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;

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.

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.

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.).

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. A self-vehicle speed estimation method suitable for multiple scenarios, characterized in that the method includes the following steps: 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; 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; According to the stationary point speed distribution, the vehicle speed data corresponding to the vehicle body is generated in real time. 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: V _otg =V/cosθ (1) 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. 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: 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; 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. 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＝V _err +cosθ·lon+sinθ·lat (2) Among them, V _err is the minimum baseline error, lon is the longitudinal speed correction, and lat is the transverse speed correction. 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: 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; 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. 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: 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; 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; 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. 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: Combined with linear filtering, the deflection angle data corresponding to the self-body body is smoothed; 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. 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: 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; 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; 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. 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: V _otg =V/cosθ (1) 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; The first generation unit also includes: 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; 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; The calculation formula of the first threshold value is as follows: Threshold1＝V _err +cosθ·lon+sinθ·lat (2) Among them, V _err is the minimum baseline error, lon is the longitudinal velocity correction, and lat is the transverse velocity correction; And/or, the first generating unit further includes: 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; 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; And/or, the first generation unit also includes: 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; 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; 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; And/or, the second generation unit also includes: The first processing module is used to combine linear filtering to smooth the deflection angle data corresponding to the self-body; 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. 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.