CN116577787A - Vehicle motion state parameter estimation method based on vehicle millimeter wave radar - Google Patents
Vehicle motion state parameter estimation method based on vehicle millimeter wave radar Download PDFInfo
- Publication number
- CN116577787A CN116577787A CN202310618427.7A CN202310618427A CN116577787A CN 116577787 A CN116577787 A CN 116577787A CN 202310618427 A CN202310618427 A CN 202310618427A CN 116577787 A CN116577787 A CN 116577787A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- frame
- omega
- radar
- millimeter wave
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000001514 detection method Methods 0.000 claims abstract description 7
- 125000000205 L-threonino group Chemical group [H]OC(=O)[C@@]([H])(N([H])[*])[C@](C([H])([H])[H])([H])O[H] 0.000 claims description 18
- 230000003068 static effect Effects 0.000 claims description 8
- 230000001133 acceleration Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/06—Systems determining position data of a target
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a vehicle motion state parameter estimation method based on a vehicle millimeter wave radar, which comprises the following steps of: program initialization, initializing effective result frame number N valid Is 0; step 2: acquiring 1 millimeter wave radar current frame detection point clouds, defining an acquired point cloud data set as D, wherein each point cloud comprises a distance, an azimuth angle, a pitch angle and a Doppler speed, and judging an effective result frame number N valid Whether or not it is greater than a set threshold N valid_Th res . The method provided by the invention not only can accurately estimate the motion state of the own vehicle when more rest points are provided, but also can accurately estimate the motion state of the own vehicle when the rest points are not occupied or even when the rest points are very few, and compared with the existing method, the method greatly improves the systemThe method is simple to implement, has less operand and can be applied to most embedded platforms.
Description
Technical Field
The invention belongs to the technical field of automatic driving vehicle positioning, and particularly relates to an automatic motion state parameter estimation method based on a vehicle-mounted millimeter wave radar.
Background
In an automatic driving system, a motion state parameter of a vehicle in a two-dimensional plane is an essential reference quantity when the vehicle runs autonomously. The current method for calculating the speed of the vehicle in automatic driving comprises a wheel speed meter, an IMU, a satellite integrated navigation system and the like, but the methods have different defects, such as failure of the wheel speed meter under the condition that the tires of the vehicle slip, the IMU can accumulate more and more errors along with time, and the satellite navigation has no signals in a room or a tunnel and the like.
Millimeter wave radars are increasingly applied to automatic driving systems, have the functions of ranging, measuring speed and measuring angle for targets, are good in robustness under many working conditions and low in price, so that in recent years, the millimeter wave radars of vehicle equipment are used for measuring the motion parameters of a vehicle, as in document Instantaneous Ego-Motion Estimation using Doppler Radar, a method for estimating the speed and yaw rate of the vehicle body by using a single millimeter wave radar is introduced, and in patent document CN 114325682A, a vehicle speed estimation method based on a vehicle-mounted 4D millimeter wave radar, estimates the motion parameters of the vehicle body by using 1 or more millimeter wave radars. The method has good effect when the static targets in the scene are dominant, but when a plurality of moving targets exist in the scene, the speeds of the moving targets are consistent, and the static target points are few, the moving state of the vehicle can not be accurately estimated any more; in the actual use process of the vehicle, the scene appears frequently, so that if the accuracy and the robustness of the estimation of the motion parameters of the vehicle are improved, the problem to be solved is urgent.
Disclosure of Invention
The invention provides a vehicle motion state parameter estimation method based on a vehicle millimeter wave radar, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: the vehicle motion state parameter estimation method based on the vehicle millimeter wave radar specifically comprises the following steps,
step 1: program initialization, initializing effective result frame number N valid Is 0;
step 2: acquiring 1 millimeter wave radar current frame detection point clouds, defining an acquired point cloud data set as D, wherein each point cloud comprises a distance, an azimuth angle, a pitch angle and a Doppler speed, and judging an effective result frame number N valid Whether or not it is greater than a set threshold N valid_Th res If yes, directly switching to the step 6, otherwise, entering the step 3;
step 3: separating stationary and moving point clouds of the i-th frame using RANSAC (random consensus sampling);
step 4: using all the rest point information output in step 3, a least squares method is used to obtain a more accurate reference to radar v according to the formula in step 3-2 x And v y Is determined by the estimation of (a);
step 5: v storing the current ith frame i And omega i Acquiring radar point cloud data of the (i+1) th frame, and calculating v of the (i+1) th frame by using the steps 3 and 4 i+1 And omega i+1 ；
Step 6: acquiring the radar detection point cloud of the latest frame, and obtaining the latest v related to the vehicle by using the methods in the step 2 and the step 3 i And omega i ；
Step 7: v of radar saved by last frame x,i-1 And v y,i-1 And calculating the theoretical Doppler speed of each point in the current frame, and then calculating the absolute value of the difference value between the theoretical Doppler speed and the actual Doppler speed.
Preferably, the specific step of the step 3 is that,
s3.1, randomly selecting K points from a point cloud data set D of a current frame, wherein 2< K < N, and N is the number of point clouds in the data set D;
s3.2, using the Doppler velocity, azimuth angle and pitch angle information of the K points to construct an equation as follows,
wherein v is r,j 、θ j And phi j The Doppler velocity, azimuth angle and pitch angle of the jth point are respectively j E [ 0K ]]，v x And v y The velocity components of the radar in the X-axis and Y-axis of the vehicle coordinate system, respectively, and v x ＝-cos(α)v S ，v y ＝-sin(α)v S Wherein alpha is the included angle between the radar and the X axis of the vehicle coordinate system, v S Is the movement speed of the radar;
s3.3 solving v for the equation in step S3.2 using least squares x And v y And use v x And v y Calculating theoretical Doppler speeds of all points in the current frame, and then calculating the absolute value of the errors of the theoretical Doppler speeds and the actual Doppler speeds of each point, wherein the absolute value of the errors is smaller than a set threshold V err_Thres The points of (1) are marked as rest points, and the number of the rest points obtained by the estimation at the time is counted;
s3.4, repeating the steps S3.1 to S3.3 until the designated iteration number N is satisfied iter And then, selecting the estimation result with the maximum number of stationary points as output, and outputting all corresponding stationary point information.
Preferably, the specific calculation formula in the step 4 is as follows:
where v and ω are the speed of the vehicle along the X-axis at the time corresponding to this frame and the Yaw Rate Yaw Rate, l and b are the longitudinal and lateral distances of the radar from the origin of the vehicle coordinate system, respectively, and β is the azimuth mounting angle of the radar from the vehicle coordinate system.
Preferably, the specific content of the step 5 is:
v storing the current ith frame i And omega i Acquiring radar point cloud data of the (i+1) th frame, and calculating v of the (i+1) th frame by using the steps 3 and 4 i+1 And omega i+1 Setting a speed threshold V related to frames according to the maximum acceleration of the vehicle and the maximum rotation angle speed of the steering wheel diff_Thres And angular velocity threshold W diff_Thres If v of the i-th frame i And omega i And v of the (i+1) th frame i+1 And omega i+1 The absolute value of the difference of (2) can be smaller than the threshold, the number of effective result frames N valid Accumulating 1, otherwise, the number of effective result frames N valid And setting 0.
Preferably, the specific content of the step 6 is:
judging the latest v i And omega i V of the vehicle of the previous frame i-1 And omega i-1 Whether the absolute value of the difference value of (c) satisfies a set speed threshold V diff_Thres And angular velocity threshold W diff_Thres If so, the latest v about the vehicle is output i And omega i If not, go to step 7.
Preferably, the specific content of the step 7 is:
calculating the theoretical Doppler velocity of each point in the current frame, calculating the absolute value of the difference between the theoretical Doppler velocity and the measured Doppler velocity, and if the absolute value of the difference is smaller than the set threshold V Doppler_diff_Thres Recording the point cloud as a rest point, counting the number of the rest points in the frame, and if the total number of the rest points is larger than a set threshold N static Then the v of the current frame is calculated using the obtained rest point and the formula in step 3-2 x,i And v y,i And using the formula in the step 4 to obtain v of the vehicle corresponding to the current frame i And omega i The method comprises the steps of carrying out a first treatment on the surface of the If the number of static points does not meet the set threshold, then v of the last frame is used i-1 And omega i-1 V as current frame vehicle i And omega i And output.
The beneficial effects of adopting above technical scheme are:
the method not only can accurately estimate the motion state of the own vehicle when more rest points are available, but also can accurately estimate the motion state of the own vehicle when the rest points are not occupied or even when the rest points are few, and compared with the existing method, the method greatly improves the robustness of the system, is easy to realize, has less operation amount and can be applied to most embedded platforms.
Drawings
FIG. 1 is a flow chart of a vehicle motion parameter estimation method based on a vehicle millimeter wave radar disclosed by the invention;
FIG. 2 is a schematic diagram of a vehicle coordinate system in the vehicle motion parameter estimation method based on the vehicle millimeter wave radar;
FIG. 3 is a schematic diagram II of a vehicle coordinate system in the vehicle motion parameter estimation method based on the vehicle millimeter wave radar;
fig. 4 is a diagram of actual effect of vehicle from stationary to moving in the vehicle motion parameter estimation method based on the vehicle millimeter wave radar disclosed by the invention;
FIG. 5 is a graph of actual effects of two turns of a vehicle in the running process of the vehicle in the vehicle-mounted millimeter wave radar-based vehicle motion parameter estimation method;
Detailed Description
The following detailed description of the embodiments of the invention, given by way of example only, is presented in the accompanying drawings to aid in a more complete, accurate and thorough understanding of the concepts and aspects of the invention, and to aid in its practice, by those skilled in the art.
As shown in fig. 1 to 5, the method for estimating the motion state parameter of the vehicle based on the vehicle millimeter wave radar can accurately estimate the motion state of the vehicle when the number of the rest points is large, can accurately estimate the motion state of the vehicle when the number of the rest points is small even when the number of the rest points is small, greatly improves the robustness of the system compared with the existing method, is easy to realize, has less operand and can be applied to most embedded platforms.
Specifically, as shown in fig. 1 to 5, specifically includes the following steps,
step 1: program initialization, initializing effective result frame number N valid Is 0;
step 2: acquiring 1 millimeter wave radar current frame detection point clouds, defining an acquired point cloud data set as D, wherein each point cloud comprises a distance, an azimuth angle, a pitch angle and a Doppler speed, and judging an effective result frame number N valid Whether or not it is greater than a set threshold N valid_Th res If yes, directly switching to the step 6, otherwise, entering the step 3;
step 3: separating stationary and moving point clouds of the i-th frame using RANSAC (random consensus sampling);
step 4: using all the rest point information output in step 3, a least squares method is used to obtain a more accurate reference to radar v according to the formula in step 3-2 x And v y Is determined by the estimation of (a);
step 5: v storing the current ith frame i And omega i Acquiring radar point cloud data of the (i+1) th frame, and calculating v of the (i+1) th frame by using the steps 3 and 4 i+1 And omega i+1 ；
Step 6: acquiring the radar detection point cloud of the latest frame, and obtaining the latest v related to the vehicle by using the methods in the step 2 and the step 3 i And omega i ；
Step 7: v of radar saved by last frame x,i-1 And v y,i-1 And calculating the theoretical Doppler speed of each point in the current frame, and then calculating the absolute value of the difference value between the theoretical Doppler speed and the actual Doppler speed.
The specific steps of the step 3 are that,
s3.1, randomly selecting K points from a point cloud data set D of a current frame, wherein 2< K < N, and N is the number of point clouds in the data set D;
s3.2, using the Doppler velocity, azimuth angle and pitch angle information of the K points to construct an equation as follows,
wherein v is r,j 、θ j And phi j The Doppler velocity, azimuth angle and pitch angle of the jth point are respectively j E [ 0K ]]，v x And v y The velocity components of the radar in the X-axis and Y-axis of the vehicle coordinate system, respectively, and v x ＝-cos(α)v S ，v y ＝-sin(α)v S Wherein alpha is the included angle between the radar and the X axis of the vehicle coordinate system, v S Is the movement speed of the radar;
s3.3 solving v for the equation in step S3.2 using least squares x And v y And use v x And v y Calculating theoretical Doppler speeds of all points in the current frame, and then calculating the absolute value of the errors of the theoretical Doppler speeds and the actual Doppler speeds of each point, wherein the absolute value of the errors is smaller than a set threshold V err_Thres The points of (1) are marked as rest points, and the number of the rest points obtained by the estimation at the time is counted;
s3.4, repeating the steps S3.1 to S3.3 until the designated iteration number N is satisfied iter And then, selecting the estimation result with the maximum number of stationary points as output, and outputting all corresponding stationary point information.
The specific calculation formula in the step 4 is as follows: where v and ω are the speed of the vehicle along the X-axis at the time corresponding to this frame and the Yaw Rate Yaw Rate, l and b are the longitudinal and lateral distances of the radar from the origin of the vehicle coordinate system, respectively, and β is the azimuth mounting angle of the radar from the vehicle coordinate system.
The specific content of the step 5 is as follows: v storing the current ith frame i And omega i Acquiring radar point cloud data of the (i+1) th frame, and calculating v of the (i+1) th frame by using the steps 3 and 4 i+1 And omega i+1 Setting a speed threshold V related to frames according to the maximum acceleration of the vehicle and the maximum rotation angle speed of the steering wheel diff_Thres And angular velocity threshold W diff_Thres If (3)V of the ith frame i And omega i And v of the (i+1) th frame i+1 And omega i+1 The absolute value of the difference of (2) can be smaller than the threshold, the number of effective result frames N valid Accumulating 1, otherwise, the number of effective result frames N valid And setting 0.
The specific content of the step 6 is as follows: judging the latest v i And omega i V of the vehicle of the previous frame i-1 And omega i-1 Whether the absolute value of the difference value of (c) satisfies a set speed threshold V diff_Thres And angular velocity threshold W diff_Thres If so, the latest v about the vehicle is output i And omega i If not, go to step 7.
The specific content of the step 7 is as follows: calculating the theoretical Doppler velocity of each point in the current frame, calculating the absolute value of the difference between the theoretical Doppler velocity and the measured Doppler velocity, and if the absolute value of the difference is smaller than the set threshold V Doppler_diff_Thres Recording the point cloud as a rest point, counting the number of the rest points in the frame, and if the total number of the rest points is larger than a set threshold N static Then the v of the current frame is calculated using the obtained rest point and the formula in step 3-2 x,i And v y,i And using the formula in the step 4 to obtain v of the vehicle corresponding to the current frame i And omega i The method comprises the steps of carrying out a first treatment on the surface of the If the number of static points does not meet the set threshold, then v of the last frame is used i-1 And omega i-1 V as current frame vehicle i And omega i And output.
While the invention has been described above by way of example with reference to the accompanying drawings, it is to be understood that the invention is not limited to the particular embodiments described, but is capable of numerous insubstantial modifications of the inventive concept and solution; or the invention is not improved, and the conception and the technical scheme are directly applied to other occasions and are all within the protection scope of the invention.
Claims (6)
1. A vehicle motion state parameter estimation method based on a vehicle millimeter wave radar is characterized in that: in particular comprising the following steps of the method,
step 1: program initialization, initializing effective result frame number N valid Is 0;
step 2: acquiring 1 millimeter wave radar current frame detection point clouds, defining an acquired point cloud data set as D, wherein each point cloud comprises a distance, an azimuth angle, a pitch angle and a Doppler speed, and judging an effective result frame number N valid Whether or not it is greater than a set threshold N valid_Th res If yes, directly switching to the step 6, otherwise, entering the step 3;
step 3: separating stationary and moving point clouds of the i-th frame using RANSAC (random consensus sampling);
step 4: using all the rest point information output in step 3, a least squares method is used to obtain a more accurate reference to radar v according to the formula in step 3-2 x And v y Is determined by the estimation of (a);
step 5: v storing the current ith frame i And omega i Acquiring radar point cloud data of the (i+1) th frame, and calculating v of the (i+1) th frame by using the steps 3 and 4 i+1 And omega i+1 ；
Step 6: acquiring the radar detection point cloud of the latest frame, and obtaining the latest v related to the vehicle by using the methods in the step 2 and the step 3 i And omega i ；
Step 7: v of radar saved by last frame x,i-1 And v y,i-1 And calculating the theoretical Doppler speed of each point in the current frame, and then calculating the absolute value of the difference value between the theoretical Doppler speed and the actual Doppler speed.
2. The vehicle motion state parameter estimation method based on the vehicle millimeter wave radar according to claim 1, wherein the method comprises the following steps: the specific steps of the step 3 are that,
s3.1, randomly selecting K points from a point cloud data set D of a current frame, wherein 2< K < N, and N is the number of point clouds in the data set D;
s3.2, using the Doppler velocity, azimuth angle and pitch angle information of the K points to construct an equation as follows,
wherein v is r,j 、θ j And phi j The Doppler velocity, azimuth angle and pitch angle of the jth point are respectively j E [ 0K ]]，v x And v y The velocity components of the radar in the X-axis and Y-axis of the vehicle coordinate system, respectively, and v x ＝-cos(α)v S ，v y ＝-sin(α)v S Wherein alpha is the angle between the radar and the X axis of the vehicle coordinate system,
v S is the movement speed of the radar;
s3.3 solving v for the equation in step S3.2 using least squares x And v y And use v x And v y Calculating theoretical Doppler speeds of all points in the current frame, and then calculating the absolute value of the errors of the theoretical Doppler speeds and the actual Doppler speeds of each point, wherein the absolute value of the errors is smaller than a set threshold V err_Thres The points of (1) are marked as rest points, and the number of the rest points obtained by the estimation at the time is counted;
s3.4, repeating the steps S3.1 to S3.3 until the designated iteration number N is satisfied iter And then, selecting the estimation result with the maximum number of stationary points as output, and outputting all corresponding stationary point information.
3. The vehicle motion state parameter estimation method based on the vehicle millimeter wave radar according to claim 1, wherein the method comprises the following steps: the specific calculation formula in the step 4 is as follows:
where v and ω are the speed of the vehicle along the X-axis at the time corresponding to this frame and the Yaw Rate Yaw Rate, l and b are the longitudinal and lateral distances of the radar from the origin of the vehicle coordinate system, respectively, and β is the azimuth mounting angle of the radar from the vehicle coordinate system.
4. The vehicle motion state parameter estimation method based on the vehicle millimeter wave radar according to claim 1, wherein the method comprises the following steps: the specific content of the step 5 is as follows:
v storing the current ith frame i And omega i Acquiring radar point cloud data of the (i+1) th frame, and calculating v of the (i+1) th frame by using the steps 3 and 4 i+1 And omega i+1 Setting a speed threshold V related to frames according to the maximum acceleration of the vehicle and the maximum rotation angle speed of the steering wheel diff_Thres And angular velocity threshold W diff_Thres If v of the i-th frame i And omega i And v of the (i+1) th frame i+1 And omega i+1 The absolute value of the difference of (2) can be smaller than the threshold, the number of effective result frames N valid Accumulating 1, otherwise, the number of effective result frames N valid And setting 0.
5. The vehicle motion state parameter estimation method based on the vehicle millimeter wave radar according to claim 1, wherein the method comprises the following steps: the specific content of the step 6 is as follows:
judging the latest v i And omega i V of the vehicle of the previous frame i-1 And omega i-1 Whether the absolute value of the difference value of (c) satisfies a set speed threshold V diff_Thres And angular velocity threshold W diff_Thres If so, the latest v about the vehicle is output i And omega i If not, go to step 7.
6. The vehicle motion state parameter estimation method based on the vehicle millimeter wave radar according to claim 1, wherein the method comprises the following steps: the specific content of the step 7 is as follows:
calculating the theoretical Doppler velocity of each point in the current frame, calculating the absolute value of the difference between the theoretical Doppler velocity and the measured Doppler velocity, and if the absolute value of the difference is smaller than the set threshold V Doppler_diff_Thres Recording the point cloud as a rest point, counting the number of the rest points in the frame, and if the total number of the rest points is larger than a set threshold N static Then the v of the current frame is calculated using the obtained rest point and the formula in step 3-2 x,i And v y,i And using the formula in the step 4 to obtain v of the vehicle corresponding to the current frame i And omega i The method comprises the steps of carrying out a first treatment on the surface of the If the number of static points does not meet the set threshold, then v of the last frame is used i-1 And omega i-1 V as current frame vehicle i And omega i And output.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310618427.7A CN116577787A (en) | 2023-05-30 | 2023-05-30 | Vehicle motion state parameter estimation method based on vehicle millimeter wave radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310618427.7A CN116577787A (en) | 2023-05-30 | 2023-05-30 | Vehicle motion state parameter estimation method based on vehicle millimeter wave radar |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116577787A true CN116577787A (en) | 2023-08-11 |
Family
ID=87533890
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310618427.7A Pending CN116577787A (en) | 2023-05-30 | 2023-05-30 | Vehicle motion state parameter estimation method based on vehicle millimeter wave radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116577787A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116859406A (en) * | 2023-09-05 | 2023-10-10 | 武汉煜炜光学科技有限公司 | Calculation method and device for vehicle speed based on laser radar |
| CN118244257A (en) * | 2024-05-24 | 2024-06-25 | 苏州大学 | Vehicle state evaluation method and system based on millimeter wave radar |
-
2023
- 2023-05-30 CN CN202310618427.7A patent/CN116577787A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116859406A (en) * | 2023-09-05 | 2023-10-10 | 武汉煜炜光学科技有限公司 | Calculation method and device for vehicle speed based on laser radar |
| CN116859406B (en) * | 2023-09-05 | 2023-11-28 | 武汉煜炜光学科技有限公司 | Calculation method and device for vehicle speed based on laser radar |
| CN118244257A (en) * | 2024-05-24 | 2024-06-25 | 苏州大学 | Vehicle state evaluation method and system based on millimeter wave radar |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116577787A (en) | Vehicle motion state parameter estimation method based on vehicle millimeter wave radar | |
| CN110531357B (en) | Method for estimating speed of moving target in horizontal plane and radar detection system | |
| CN110672093B (en) | Vehicle navigation positioning method based on UWB and inertial navigation fusion | |
| CN110515051B (en) | Method and system for measuring installation angle value of vehicle-mounted radar | |
| CN107274721B (en) | Multi-vehicle cooperative positioning method in intelligent transportation system | |
| CN113721234B (en) | Dynamic-static separation filtering method and device for vehicle-mounted millimeter wave Lei Dadian cloud data | |
| CN113933818A (en) | Method, device, storage medium and program product for calibrating laser radar external parameter | |
| CN108508471A (en) | A kind of automatic driving vehicle localization method and device | |
| CN116907412B (en) | Agricultural machinery row spacing deviation detection method, device and system | |
| CN112326990A (en) | Method and system for measuring speed of working vehicle | |
| CN112710339A (en) | Method and apparatus for calibrating vehicle sensors | |
| CN110988873B (en) | Single-channel SAR ship speed estimation method and system based on energy center extraction | |
| CN113552546B (en) | Calibration method and device for mounting angle of vehicle-mounted radar | |
| CN111731309B (en) | Gradient estimation method, device and equipment and vehicle | |
| CN113763434B (en) | Target track prediction method based on Kalman filtering multi-motion model switching | |
| CN113296139A (en) | Self-adaptive image optical flow and RTK fusion attitude determination method | |
| CN110542902A (en) | Simple height measurement method suitable for vehicle-mounted millimeter wave radar | |
| CN113569430B (en) | Method for identifying reentry flight turning direction under external measurement-only observation | |
| EP4365631A1 (en) | A radar system for 3d ego motion estimation | |
| US11965978B2 (en) | Calibration pipeline for estimating six degrees of freedom (6DoF) alignment parameters for an autonomous vehicle | |
| CN116224281B (en) | Vehicle attitude information determining method, device and equipment | |
| CN113706854B (en) | Vehicle cooperative positioning method in intelligent Internet of vehicles | |
| CN112525143B (en) | Method for determining installation angle of equipment and vehicle-mounted terminal | |
| US11977149B2 (en) | Filtering and aggregating detection points of a radar point cloud for an autonomous vehicle | |
| CN118311561A (en) | Fusion positioning method of laser radar and millimeter wave radar and related equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |