CN112683260A - High-precision map and V2X-based integrated navigation positioning precision improving system and method - Google Patents
High-precision map and V2X-based integrated navigation positioning precision improving system and method Download PDFInfo
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Abstract
The invention discloses a combined navigation positioning precision improving system and method based on a high-precision map and V2X. In the running process of the vehicle, inertial navigation positioning information of the vehicle is obtained through the combined inertial navigation device, the relative distance and the angle between the vehicle and a marker around the vehicle are obtained through the detection device, the actual positioning information of the vehicle is determined according to the actual coordinates of the marker in the high-precision map and the relative distance and the angle, and the inertial navigation positioning information, the actual positioning information and the self-vehicle positioning information are fused to obtain the accurate positioning information of the vehicle. The method can improve the positioning accuracy of the combined inertial navigation under severe working conditions, solves the problem that the positioning accuracy is greatly reduced due to IMU positioning information divergence under the condition that the combined inertial navigation equipment is unlocked, and provides feasibility for mass production and carrying of positioning equipment for automatic driving vehicles.
Description
Technical Field
The invention belongs to the technical field of automobiles, and particularly relates to a combined navigation positioning precision improving system and method based on a high-precision map and V2X.
Background
With the heat of fire in the field of autopilot, maps and positioning are gradually stepping into the public view in autopilot applications. The common navigation map only has meter-level positioning accuracy, which is naturally not satisfied by an automatic driving automobile. The high-precision map has centimeter-level precision and richer information compared with a navigation map. However, the map with high precision is not enough, and the high-precision positioning is also a problem which needs to be solved urgently in automatic driving. With the development of the technology, the positioning accuracy of the combined inertial navigation product is continuously improved, but the cost factor is considered, the accuracy of the positioning equipment applied to the mass production of automobiles is not too high, and most of the positioning equipment is the combined inertial navigation of the MEMS. The combined navigation is mostly composed of GNSS and IMU, and has many factors influencing the positioning performance of the combined inertial navigation, and the positioning performance of the combined inertial navigation can be influenced by excessive surrounding high-rise buildings, vehicles running under an overhead bridge, vehicles running in a long tunnel and the like. Under the working conditions, the GNSS satellite searching capability is reduced, and the positioning accuracy brought by the GNSS is sharply reduced due to insufficient satellite number. Although the IMU is not influenced by the environment, the positioning accuracy of the IMU continuously diverges as time increases, so that the current integrated navigation product is not enough to independently meet the positioning requirement of automatic driving. In an open scene, the combined inertial navigation of the MEMS can support centimeter-level positioning, but for vehicles, the automobile needs positioning equipment to realize accurate positioning under severe working conditions. With the development of high-precision map technology, map makers also collect high-precision maps of a large number of roads in the country at present.
For a vehicle enterprise, the cost factor of mass production is one ring that must be considered. Therefore, it is not practical to mount the laser radar and the fiber-optic inertial navigation system without reducing the cost. Vehicles have many other sensors such as cameras and millimeter wave radars. And under the condition that the future high-precision map technology is mature, the high-precision map and the data fusion of the sensors enable the improvement of the positioning performance of the combined inertial navigation to be possible. On the basis of sensor data fusion, V2X data fusion is introduced, and the positioning accuracy is further improved.
Therefore, the method for improving the combined navigation positioning precision under the severe working conditions, which is suitable for the field of intelligent automobiles, is of great significance.
Disclosure of Invention
The invention aims to solve the defects in the background technology, provides a combined navigation positioning precision improving system and method based on a high-precision map and V2X, is suitable for the field of automatic driving, solves the problem that the positioning precision of combined inertial navigation is reduced under severe working conditions, and can provide a positioning reference basis for an automatic driving vehicle.
The technical scheme adopted by the invention is as follows: a combined navigation positioning precision improving system based on a high-precision map and V2X comprises
The combined inertial navigation module is used for acquiring inertial navigation positioning information of the vehicle and sending the inertial navigation positioning information to the data processing module;
the vehicle-mounted detection equipment acquires the actual relative distance between the vehicle and the markers around the vehicle and sends the actual relative distance to the high-precision map module;
the high-precision map module is used for determining the actual positioning information of the vehicle according to the actual coordinates of the markers and the actual relative distance in the high-precision map and sending the actual positioning information to the data processing module;
the V2X module is used for obtaining the self-vehicle positioning information according to the positioning information of the surrounding vehicles and the distance between the vehicles and sending the self-vehicle positioning information to the data processing module;
and the data processing module is used for fusing the received inertial navigation positioning information, the actual positioning information and the self-vehicle positioning information to obtain the accurate positioning information of the vehicle.
Further, the detection device comprises a radar and a camera.
Further, the camera obtains a first relative distance between the camera and the marker, the radar obtains a second relative distance between the radar and the marker, regression analysis is carried out on the first relative distance and the two relative distances, actual relative distances between the radar and the marker are obtained through fitting, and actual positioning information is obtained through inverse calculation of the actual coordinates of the marker and the actual relative distances in the high-precision map.
Furthermore, the fusion processing includes performing weight distribution on confidence coefficients of the inertial navigation positioning information, the actual positioning information and the self-vehicle positioning information, and performing deviation rectification on the inertial navigation positioning information through the actual positioning information and the self-vehicle positioning information based on the inertial navigation positioning information and the weight coefficient to obtain accurate positioning information.
Further, the marker comprises at least two of a lane line, a lamp post, a sign and a building.
During the running process of a vehicle, inertial navigation positioning information of the vehicle is obtained through a combined inertial navigation device, the relative distance and angle between the vehicle and markers around the vehicle are obtained through a detection device, the actual positioning information of the vehicle is determined according to the actual coordinates of the markers in the high-precision map and the relative distance and angle, and the inertial navigation positioning information, the actual positioning information and the vehicle positioning information are fused to obtain the accurate positioning information of the vehicle.
Further, the detection device comprises a radar and a camera.
Further, the camera obtains a first relative distance between the camera and the marker, the radar obtains a second relative distance between the radar and the marker, regression analysis is carried out on the first relative distance and the two relative distances, actual relative distances between the radar and the marker are obtained through fitting, and actual positioning information is obtained through inverse calculation of the actual coordinates of the marker and the actual relative distances in the high-precision map.
Furthermore, the fusion processing includes performing weight distribution on confidence coefficients of the inertial navigation positioning information, the actual positioning information and the self-vehicle positioning information, and performing deviation rectification on the inertial navigation positioning information through the actual positioning information and the self-vehicle positioning information based on the inertial navigation positioning information and the weight coefficient to obtain accurate positioning information.
Still further, the signs include at least two of lane lines, light poles, signs, and buildings.
The invention introduces a high-precision map as a true value, obtains the relative distance of ground objects through the original sensor camera and the millimeter wave radar of the vehicle, obtains the auxiliary vehicle positioning information through inverse calculation, and can correct the combined navigation data by the data to obtain the positioning information with higher precision. Peripheral positioning information is obtained through the V2X system, the positioning information of the vehicle is further corrected, and the positioning result is more accurate.
The method can carry mass-produced vehicles, has reference significance for the field of automatic driving, can improve the positioning accuracy of the combined inertial navigation under severe working conditions, solves the problem that the positioning accuracy is greatly reduced due to IMU positioning information divergence under the condition that the combined inertial navigation equipment is unlocked, and provides feasibility for mass-produced carrying of the positioning equipment by the automatic driving vehicles.
Drawings
Fig. 1 is a schematic diagram of a positioning accuracy improving system according to the present invention.
FIG. 2 is a schematic view of the positioning error analysis of the present invention.
FIG. 3 is a schematic diagram of the fusion process of the present invention.
FIG. 4 is a block diagram of a multi-information fusion process according to the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in FIG. 1, the invention provides a combined navigation positioning precision improving system based on a high-precision map and V2X, which comprises
The combined inertial navigation module is used for acquiring inertial navigation positioning information of the vehicle and sending the inertial navigation positioning information to the data processing module;
the vehicle-mounted detection equipment acquires the actual relative distance between the vehicle and the markers around the vehicle and sends the actual relative distance to the high-precision map module;
the high-precision map module is used for determining the actual positioning information of the vehicle according to the actual coordinates of the markers and the actual relative distance in the high-precision map and sending the actual positioning information to the data processing module;
the V2X module is used for obtaining the self-vehicle positioning information according to the positioning information of the surrounding vehicles and the distance between the vehicles and sending the self-vehicle positioning information to the data processing module;
and the data processing module is used for fusing the received inertial navigation positioning information, the actual positioning information and the self-vehicle positioning information to obtain the accurate positioning information of the vehicle.
The method for realizing the positioning precision improvement based on the positioning precision improvement system has the following specific technical scheme:
the first step is as follows: the vehicle is provided with a high-precision map module, a V2X module, a combined inertial navigation module and vehicle-mounted detection equipment (a forward-looking camera system and a millimeter wave radar system).
The second step is that: and when the vehicle runs in an area with a high-precision map, acquiring positioning information of the vehicle, and acquiring inertial navigation positioning data L1 from the combined inertial navigation module. In the driving process, continuously acquiring a first relative distance D1 between the front-view camera and ground objects (signs, lamp posts and the like) on a road; and continuously acquiring a second relative distance D2 from the millimeter-wave radar to the road ground object.
The third step: and (6) data processing. Since the camera and the millimeter wave radar are less accurate than the data of the laser radar, error analysis needs to be introduced. Where the error analysis is shown in figure 2. The errors comprise errors along with time when the positioning accuracy of the combined inertial navigation module is reduced, errors generated by camera ranging, errors generated by millimeter wave radar ranging and errors of high-accuracy map module drawing. And performing data fusion on the data obtained by the camera and the data obtained by the millimeter waves to obtain a relatively reliable actual relative distance D3 between the camera and the ground object, and performing back calculation according to the actual coordinates of the ground object in the high-precision map to obtain the real-time positioning data L2 of the vehicle. And performing Kalman filtering calculation on the positioning track L1 obtained by inertial navigation and the positioning track L2 obtained by inverse calculation of the high-precision map to obtain final actual positioning data, and feeding the final actual positioning data back to the vehicle driving controller.
The fourth step: through V2X module, constantly acquire the locating information of peripheral vehicle and peripheral infrastructure, further revise the locating information of own car according to the locating data of V2X module to improve the positioning performance that the combination was used to lead, the locating data of this moment feedback compare in solitary combination and are used to lead information, and the precision has certain promotion.
As shown in fig. 3, it is shown how data is obtained and back-calculated to obtain positioning information.
The first step is as follows: position data is obtained from the combined inertial navigation (x1, y1, z 1).
The second step is that: the camera obtains the relative distance D1. And acquiring relevant parameters of the camera, such as the height h of the camera, the pitch angle theta of the detector, the vertical half field angle beta of the detector and the horizontal field angle gamma of the detector.
The third step: the millimeter wave radar obtains the relative distance D2. The radar transmits continuous waves with variable frequency in a sweep frequency period, echoes reflected by an object have a certain frequency difference with a transmitted signal, and distance information between a target and the radar can be obtained by measuring the frequency difference.
The fourth step: regression analysis was performed on D1 and D2 to obtain median error values. And fitting to obtain a relatively accurate relative distance D and a relative direction sigma.
The fifth step: obtaining real coordinate values (x, y, z) of the target ground object from the high-precision map, and obtaining the positioning information (x ', y ', z ') of the self-vehicle through inverse calculation
x2＝x-D*σ(x)
y2＝y-D*σ(y)
z2＝z-D*σ(z)
And a sixth step: the distance Dv between the nearby vehicle positioning information (Xn, Yn, Zn) and the vehicle is acquired from V2X, and correction of the own vehicle positioning information is assisted.
x3n＝Xn-Dv(x)
y3n＝Yn-Dv(y)
z3n＝Zn-Dv(z)
The seventh step: and the inertial navigation positioning information, the high-precision map back-calculation positioning information and the V2X positioning information are combined for redundant fusion, so that the positioning precision is further improved, and the vehicle positioning information is obtained.
As shown in fig. 4, an algorithm framework of various information fusion is shown, and the implementation process is as follows:
the first step is as follows: the self-vehicle positioning data obtained from the combined inertial navigation is the state equation 1, which is a commonly used positioning means in the prior art, and is greatly influenced by a GPS signal, and if the lock is lost for a long time, a large deviation is caused.
The second step is that: the own vehicle positioning data obtained from the camera through the high-precision map back calculation is a state equation 2. Due to the fact that the ground object objects exist, the vehicle positioning information points can be returned. And calculating the Euclidean distance between the points, and assuming a virtual point to ensure that the Euclidean distance from the virtual point to all the other positioning information points is the minimum, wherein the positioning point is the positioning information point obtained by the reverse calculation of the camera at the moment.
The third step: the own vehicle positioning data obtained from the millimeter wave radar through the reverse calculation of the high-precision map is a state equation 3. And a plurality of vehicle ground feature information points can be returned due to a plurality of target ground features. And calculating the Euclidean distance between each point, and assuming a virtual point to ensure that the Euclidean distance from the virtual point to all the other positioning information points is the minimum, wherein the positioning point is the positioning information point obtained by reverse calculation of the millimeter wave radar.
The fourth step: and carrying out sensor fault detection on the combined inertial navigation information, the camera back calculation information and the millimeter wave radar back calculation information. And if the sensor fault is detected, feeding back the corresponding sensor, and setting the confidence coefficient of the sensor data to be 0. If all is normal, the weight is output to the weight distribution module.
The fifth step: the surrounding V2X vehicle sends the positioning information to the host vehicle in real time. This information is based on the number of surrounding vehicles, and the larger the number, the more positioning information is fed back, and also a point is found so that the sum of euclidean distances is minimized, and this point is defined as a positioning information point back calculated by V2X. And output to the weight assignment module.
And a sixth step: and carrying out weight distribution according to the confidence degrees of the four information. Based on the positioning information directly output by the combined inertial navigation, the camera, the radar and the V2X information continuously correct the deviation of the positioning information, and return a relative distance value in real time, wherein the relative distance value is verified by the sensor and the V2X, and the confidence of corresponding data is dynamically modified.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Those not described in detail in this specification are within the skill of the art.
Claims (10)
1. A combined navigation positioning accuracy improving system based on a high-accuracy map and V2X is characterized in that: comprises that
The combined inertial navigation module is used for acquiring inertial navigation positioning information of the vehicle and sending the inertial navigation positioning information to the data processing module;
the vehicle-mounted detection equipment acquires the actual relative distance between the vehicle and the markers around the vehicle and sends the actual relative distance to the high-precision map module;
the high-precision map module is used for determining the actual positioning information of the vehicle according to the actual coordinates of the markers and the actual relative distance in the high-precision map and sending the actual positioning information to the data processing module;
the V2X module is used for obtaining the self-vehicle positioning information according to the positioning information of the surrounding vehicles and the distance between the vehicles and sending the self-vehicle positioning information to the data processing module;
and the data processing module is used for fusing the received inertial navigation positioning information, the actual positioning information and the self-vehicle positioning information to obtain the accurate positioning information of the vehicle.
2. The integrated navigation positioning accuracy improving method based on the high-accuracy map and V2X, according to claim 1, wherein: the detection device comprises a radar and a camera.
3. The integrated navigation positioning accuracy improving method based on the high-accuracy map and V2X, according to claim 2, wherein: the camera obtains a first relative distance between the camera and the marker, the radar obtains a second relative distance between the radar and the marker, regression analysis is carried out on the first relative distance and the two relative distances, actual relative distances between the radar and the marker are obtained through fitting, and actual positioning information is obtained through inverse calculation of real coordinates of the marker and the actual relative distances in the high-precision map.
4. The integrated navigation positioning accuracy improving method based on the high-accuracy map and V2X, according to claim 2, wherein: the marker comprises at least two of a lane line, a lamp post, a sign and a building.
5. The integrated navigation positioning accuracy improving method based on the high-accuracy map and V2X, according to claim 1, wherein: the fusion processing comprises the steps of carrying out weight distribution on confidence coefficients of the inertial navigation positioning information, the actual positioning information and the self-vehicle positioning information, and rectifying the inertial navigation positioning information through the actual positioning information and the self-vehicle positioning information based on the inertial navigation positioning information and the weight coefficient to obtain accurate positioning information.
6. A combined navigation positioning accuracy improving method based on a high-accuracy map and V2X is characterized in that: in the running process of the vehicle, inertial navigation positioning information of the vehicle is obtained through the combined inertial navigation device, the relative distance and the angle between the vehicle and a marker around the vehicle are obtained through the detection device, the actual positioning information of the vehicle is determined according to the actual coordinate of the marker in the high-precision map and the relative distance and the angle, and the inertial navigation positioning information, the actual positioning information and the self-vehicle positioning information are fused to obtain the accurate positioning information of the vehicle.
7. The integrated navigation positioning accuracy improving system based on the high-accuracy map and V2X, according to claim 6, wherein: the detection device comprises a radar and a camera.
8. The integrated navigation positioning accuracy improving method based on the high-accuracy map and V2X, according to claim 7, wherein: the camera obtains a first relative distance between the camera and the marker, the radar obtains a second relative distance between the radar and the marker, regression analysis is carried out on the first relative distance and the two relative distances, actual relative distances between the radar and the marker are obtained through fitting, and actual positioning information is obtained through inverse calculation of real coordinates of the marker and the actual relative distances in the high-precision map.
9. The integrated navigation positioning accuracy improving method based on the high-accuracy map and V2X, according to claim 7, wherein: the marker comprises at least two of a lane line, a lamp post, a sign and a building.
10. The integrated navigation positioning accuracy improving method based on the high-accuracy map and V2X, according to claim 6, wherein: the fusion processing comprises the steps of carrying out weight distribution on confidence coefficients of the inertial navigation positioning information, the actual positioning information and the self-vehicle positioning information, and rectifying the inertial navigation positioning information through the actual positioning information and the self-vehicle positioning information based on the inertial navigation positioning information and the weight coefficient to obtain accurate positioning information.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113327443A (en) * | 2021-05-31 | 2021-08-31 | 东风汽车集团股份有限公司 | Geo-fence determination method and device for automatic driving |
| CN113945956A (en) * | 2021-10-15 | 2022-01-18 | 北京路凯智行科技有限公司 | Vehicle-mounted positioning system and mining vehicle comprising same |
| CN114754778A (en) * | 2022-04-02 | 2022-07-15 | 智道网联科技(北京)有限公司 | Vehicle positioning method and device, electronic equipment and storage medium |
| CN115046562A (en) * | 2022-04-15 | 2022-09-13 | 公安部交通管理科学研究所 | Low-cost high-precision automatic driving automobile positioning method |
| CN115164912A (en) * | 2022-06-24 | 2022-10-11 | 宁波均胜智能汽车技术研究院有限公司 | Vehicle position positioning method and device and readable storage medium |
| CN116215564A (en) * | 2023-05-10 | 2023-06-06 | 禾多科技（北京）有限公司 | Man-machine interaction intelligent driving system |
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2020
- 2020-10-30 CN CN202011192078.XA patent/CN112683260A/en active Pending
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113327443A (en) * | 2021-05-31 | 2021-08-31 | 东风汽车集团股份有限公司 | Geo-fence determination method and device for automatic driving |
| CN113945956A (en) * | 2021-10-15 | 2022-01-18 | 北京路凯智行科技有限公司 | Vehicle-mounted positioning system and mining vehicle comprising same |
| CN114754778A (en) * | 2022-04-02 | 2022-07-15 | 智道网联科技(北京)有限公司 | Vehicle positioning method and device, electronic equipment and storage medium |
| CN115046562A (en) * | 2022-04-15 | 2022-09-13 | 公安部交通管理科学研究所 | Low-cost high-precision automatic driving automobile positioning method |
| CN115046562B (en) * | 2022-04-15 | 2024-05-07 | 公安部交通管理科学研究所 | Low-cost high-precision automatic driving automobile positioning method |
| CN115164912A (en) * | 2022-06-24 | 2022-10-11 | 宁波均胜智能汽车技术研究院有限公司 | Vehicle position positioning method and device and readable storage medium |
| CN116215564A (en) * | 2023-05-10 | 2023-06-06 | 禾多科技（北京）有限公司 | Man-machine interaction intelligent driving system |
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