CN116358557A - High-precision positioning information determining method, fusion positioning module and high-precision map engine - Google Patents

Abstract

The embodiment of the specification discloses a high-precision positioning information determining method, a fusion positioning module and a high-precision map engine. The scheme may include: respectively acquiring sensor information acquired by a plurality of different types of sensors loaded on a target vehicle; acquiring first map information corresponding to the estimated position of the target vehicle and second map information within a preset distance range around the estimated position from high-precision map data; correcting the sensor information acquired by the different types of sensors based on at least one of the first map information and the second map information to obtain corrected sensor information; and fusing the corrected sensor information corresponding to the plurality of different types of sensors with the high-precision map data to obtain fused high-precision positioning information of the target vehicle. Based on the scheme, the accuracy and the robustness of the positioning system can be improved.

Description

High-precision positioning information determining method, fusion positioning module and high-precision map engine

Technical Field

The application relates to the technical field of maps, in particular to a high-precision positioning information determining method, a fusion positioning module and a high-precision map engine.

Background

With development of automatic driving technology, high-precision real-time positioning technology is increasingly important, and how to provide high-quality pose information for automatic driving has become a great point in the field of automatic driving.

At present, a high-precision positioning scheme does not reach a mature and stable state yet. In general, the data collected by the various sensors can be used to fuse the vehicle location. However, various sensors commonly used in vehicles have their own application limitations. For example, inertial measurement information may drift while stationary, RTK (real time dynamic carrier phase differential technique) signals may have signal quality that may be degraded where there is occlusion, radar may not provide effective information at locations with few or insignificant features, and so on. It is seen that it is difficult to maintain the vehicle in a highly accurate positioning state all the time, since various sensor information is affected by various external environments.

Disclosure of Invention

The embodiment of the specification provides a high-precision positioning information determining method, a fusion positioning module and a high-precision map engine, so as to solve the problem that the existing high-precision positioning method cannot keep a vehicle in a high-precision positioning state.

In order to solve the above technical problems, the embodiments of the present specification are implemented as follows:

The method for determining high-precision positioning information provided by the embodiment of the specification comprises the following steps:

respectively acquiring sensor information acquired by a plurality of different types of sensors loaded on a target vehicle;

acquiring first map information corresponding to the estimated position of the target vehicle and second map information within a preset distance range around the estimated position from high-precision map data; the first map information includes at least one of lane attribute information and road attribute information; the second map information comprises ground object attribute information;

correcting the sensor information acquired by the different types of sensors based on at least one of the first map information and the second map information to obtain corrected sensor information;

and fusing the corrected sensor information corresponding to the plurality of different types of sensors with the high-precision map data to obtain fused high-precision positioning information of the target vehicle.

The embodiment of the present disclosure provides a fusion positioning module, including:

a sensor information acquisition unit configured to acquire sensor information acquired by a plurality of different types of sensors mounted on a target vehicle, respectively;

A high-precision map information acquisition unit, configured to acquire, from high-precision map data, first map information corresponding to an estimated position of the target vehicle and second map information within a preset distance range around the estimated position; the first map information includes at least one of lane attribute information and road attribute information; the second map information comprises ground object attribute information;

the correction unit is used for respectively correcting the sensor information acquired by the different types of sensors based on at least one of the first map information and the second map information to obtain corrected sensor information;

and the data fusion unit is used for fusing the corrected sensor information corresponding to the plurality of different types of sensors with the high-precision map data to obtain fused high-precision positioning information of the target vehicle.

The embodiment of the present specification provides a high-precision map engine, including:

the fusion positioning module is used for fusing the two modules;

the electronic horizon module is used for receiving external high-precision vehicle position information and matching the external high-precision vehicle position information with a map, and providing a functional interface for automatic driving application to conduct regulation and judgment;

And at least one of an automatic driving design operation domain judging module, a map updating module, a crowdsourcing preprocessing and returning module, a path cross correlation module and a lane-level path planning module;

the automatic driving design operation domain judging module is used for configuring an automatic driving area and judging requirements;

the map updating module is used for obtaining map data updating information of the high-precision map based on the vehicle position and the planned path;

the crowd-sourced preprocessing and returning module is used for returning the cloud and updating the map data center by preprocessing such as screening, fusing and the like on UGC visual vector data;

the route cross-correlation module is used for synchronizing a global route planning result initiated by a user to the automatic driving system, and obtaining a matching route of the navigation route on the high-precision map through cross-correlation with the high-precision map;

the lane-level path planning module is used for outputting lane levels and local path planning within a certain length range in front of the vehicle according to the navigation path matching and route correction results.

One embodiment of the present disclosure can achieve at least the following advantages: by correcting the data for calculating the pose generated by each sensor by using the high-precision map data and then performing data fusion by using the corrected data, the accuracy and the robustness of the positioning system can be remarkably improved.

Drawings

In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.

Fig. 1 is a schematic overall scheme flow diagram of a method for determining high-precision positioning information according to an embodiment of the present disclosure;

FIG. 2 is a flowchart of a method for determining high-precision positioning information according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of a fused positioning module according to an embodiment of the present disclosure;

fig. 4 is a schematic structural diagram of a high-precision map engine according to an embodiment of the present disclosure.

Detailed Description

With development of automatic driving technology, high-precision real-time positioning technology is increasingly important, and how to provide high-quality pose information for automatic driving has become a great point in the field of automatic driving.

At present, a high-precision positioning scheme does not reach a mature and stable state yet. In the whole process of high-precision positioning, each sensor generates data for calculating the pose, and a fusion algorithm fuses the data to generate a high-precision position. The various sensors commonly used in vehicles have their own application limitations. In the practical application process, various sensors may have inaccurate data. For example, inertial measurement (InertialMeasurementUnit, IMU) data may drift when stationary, real-time kinematic (RTK) carrier phase differential signals may degrade where there is occlusion, and the radar may not provide effective information at locations with few or insignificant features.

In the embodiment of the specification, the error of the sensor can be corrected by using the high-precision map, and the corrected data are used for fusion, so that the accuracy and the robustness of the positioning system are greatly improved. Because the high-precision map can not be influenced by any external environment, when the signal quality of other sensors is poor, the positioning system can still maintain a high-precision positioning state by combining map constraint.

For the purposes of making the objects, technical solutions and advantages of one or more embodiments of the present specification more clear, the technical solutions of one or more embodiments of the present specification will be clearly and completely described below in connection with specific embodiments of the present specification and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present specification. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without undue burden, are intended to be within the scope of one or more embodiments herein.

It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another.

The following describes in detail the technical solutions provided by the embodiments of the present specification with reference to the accompanying drawings.

Fig. 1 is a schematic flow chart of an overall scheme of a method for determining high-precision positioning information in an embodiment of the present disclosure.

As shown in fig. 1, sensor data such as IMU, RTK, odometer sensor data (visual mileage data or radar mileage data), vehicle speed data and the like can be calibrated by using high-precision map data, so as to obtain calibrated pose information and precision factor information thereof; and inputting the calibrated pose information and the precision factor information thereof into a back-end fusion algorithm to perform information fusion, so as to obtain a fusion positioning result.

Based on the scheme of fig. 1, before external sensor data such as RTK, IMU, body signal, camera and the like are fused, high-precision map data is used for calibration, and the position deviation of the high-precision map data and the actual scene perceived by the sensor is dynamically estimated, so that the data of the sensor is corrected by the constraint of the high-precision map. Accordingly, the position information highly compatible with the high-precision map can be provided for use in automatic driving, intelligent cabins and the like.

Next, a high-precision positioning information determination method provided for the embodiments of the specification will be specifically described with reference to the drawings.

Fig. 2 is a flow chart of a method for determining high-precision positioning information according to an embodiment of the present disclosure.

From the program perspective, the execution subject of the flow may be a program installed on an application server. It is understood that the method may be performed by any apparatus, device, platform, cluster of devices having computing, processing capabilities.

As shown in fig. 2, the process may include the steps of:

step 202: sensor information acquired by a plurality of different types of sensors mounted on a target vehicle is acquired, respectively.

In the embodiments of the present specification, the corrected sensor data may be data acquired by a sensor mounted on a vehicle. Alternatively, the sensor may include a satellite positioning device (e.g., GPS device, RTK device, etc.), an inertial measurement device (InertialMeasurementUnit, IMU), a vehicle speed detection device, radar or visual odometer, etc.

Step 204: and acquiring first map information corresponding to the estimated position of the target vehicle and second map information within a preset distance range around the estimated position from the high-precision map data.

Wherein the first map information may include road attribute information or lane attribute information; the second map information may include feature attribute information. In embodiments of the present description, the surface features may be stationary objects that are positioned on the ground surface, may include naturally occurring and artificially created features, for example, features may include buildings, road signs, trees, and the like. The feature attribute information acquired from the high-precision map data may specifically include coordinate information, size information, and the like of the feature.

In practical applications, the vehicle may be repositioned over time or in accordance with a predetermined period, for example, the vehicle may be repositioned every several time intervals, and for example, the vehicle may be repositioned every several distances after the vehicle has moved, or the like. Also, each positioning may be performed on the basis of the result of the previous positioning, and more specifically, the present positioning may be performed on the basis of the result of prediction of the current vehicle position at the time of the previous positioning. The solution of fig. 2 of the present specification corresponds to a process of one vehicle positioning.

In the embodiment of the present specification, when correcting sensor data with high-precision map data, it is first necessary to acquire corresponding high-precision map data. Specifically, the corresponding high-precision map data may be acquired based on the estimated position of the target vehicle at the previous positioning.

In addition, in acquiring the high-precision map data, high-precision data (referred to as first map information in the embodiment of the present specification) corresponding to the estimated position where the vehicle is located, for example, data on the lane where the vehicle is located, such as lane width, road gradient, road curvature, lane speed limit, road speed limit, and the like, may be acquired without being limited thereto. In addition, when the high-precision map data is acquired, high-precision data (referred to as second map information in the embodiment of the present specification) within a certain distance range around the vehicle, for example, road sign information, building information, or the like, may also be acquired.

Step 206: and correcting the sensor information acquired by the different types of sensors based on at least one of the first map information and the second map information to obtain corrected sensor information.

In the embodiments of the present specification, the specific items of high-precision map data to be referred to may be different for correction of different sensors. In actual application, correction of the sensor information may be achieved using only the first map information, using only the second map information, or using both the first map information and the second map information. For example, first map information corresponding to the estimated position of the target vehicle, such as road attribute information, may be referred to, or attribute information (e.g., coordinate information) of various features around the target vehicle may be referred to, or both first map information corresponding to the estimated position of the target vehicle and attribute information of various features around the target vehicle may be referred to.

In addition, the sensor information may include sensing data, and may also include a precision factor. In practical applications, the correction of the sensor information may include correction of the sensor data itself or correction of the precision factor of the sensor data. Optionally, the sensor data in the sensor information may be corrected based on at least one of the first map information and the second map information, wherein the sensor data specifically includes at least one of the following data: satellite positioning data, inertial measurement data, radar mileage data, visual mileage data, or vehicle speed data. Alternatively, the accuracy factor in the sensor information may be corrected based on the first map information.

Step 208: and fusing the corrected sensor information corresponding to the plurality of different types of sensors with the high-precision map data to obtain fused high-precision positioning information of the target vehicle.

In embodiments of the present disclosure, pose and accuracy information may be input to a back-end fusion algorithm (e.g., extended kalman filter (ExtendedKalmanFilter, EKF)) after corrections are made to various sensors using high-accuracy map data. The fusion algorithm applies proper data to the state equation and the observation equation of the fusion algorithm according to the self requirement, and generates a fusion positioning result.

In the embodiment of the specification, the data advantage of the high-precision map is fully utilized by tightly combining the high-precision map, and the positioning scheme highly matched with the high-precision map can be realized by only using the low-cost sensor matched with the traditional automobile. The hardware cost is low, the real-time performance is good, and the mass production is easy. For the positioning scheme using richer high-cost sensors, the precision can be improved as well, and the use scene can be expanded.

It should be understood that, in the method described in one or more embodiments of the present disclosure, the order of some steps may be adjusted according to actual needs, or some steps may be omitted.

Based on the method of fig. 2, the present description examples also provide some specific implementations of the method, as described below.

In one or more embodiments, the satellite positioning information collected by the satellite positioning device may be corrected prior to data fusion positioning using the satellite positioning information. The satellite positioning information may be GPS information, RTK information, etc. In practical application, information is received from a satellite, longitude and latitude data are obtained through chip analysis, and then the analyzed longitude and latitude data and precision factors thereof are calibrated by utilizing high-precision map data, so that better positioning states can be maintained.

In one aspect, the satellite positioning data itself may be corrected based on the high-precision map information.

Specifically, the sensing data may include satellite positioning information of the target vehicle acquired by a satellite positioning device; the second map information may specifically include feature attribute information. Firstly, satellite positioning information of the target vehicle acquired by a satellite positioning device can be acquired; acquiring surrounding ground feature information of the target vehicle and relative coordinate information of the surrounding ground feature relative to the target vehicle, which are acquired by a vector camera; then, alternative object information of the target vehicle in a preset distance range around the estimated position in the high-precision map can be obtained; the estimated position is determined based on satellite positioning information of the target vehicle; determining a target ground object matched with the surrounding ground objects acquired by the vector camera from the candidate ground objects; and correcting the satellite positioning information of the target vehicle based on the absolute coordinate information of the target ground object and the relative coordinate information of the target ground object relative to the target vehicle to obtain corrected satellite positioning information.

In practical application, a camera mounted on a vehicle senses environment, and the identified image information is used for identifying vector information such as lane lines, labels and the like as camera data by using a sensing algorithm. The approximate position provided by the RTK is fused with the vehicle body data (such as signals given by the vehicle body such as the vehicle speed, the angular speed and the like) and the visual mileage acquired by the camera, so that the relatively accurate vehicle position can be obtained; collecting vector information such as high-precision lane line information, label information and the like in a high-precision map through the relatively accurate vehicle position; and comparing the camera data with the collected high-precision map information, including information such as the type of the lane line, the color of the lane line, the type of the label and the like, and calculating matching result information with highest probability by using a probability model. After providing the best matching result information, first, the coordinates of the point array of the relative position of each vector (for example, lane line, sign, etc.) in the camera data with respect to the vehicle are extracted, then the absolute coordinates of each vector (for example, lane line, sign, etc.) are extracted from the high-precision map, and the two sets of coordinate data are matched to calculate the corrected high-precision position. In this way, the positioning system can always maintain a stable high-precision positioning state.

On the other hand, the precision factor of the satellite positioning data may also be corrected based on the high-precision map information.

Specifically, the first map information may specifically include lane attribute information. Firstly, satellite positioning information of the target vehicle acquired by a satellite positioning device can be acquired; acquiring surrounding ground feature information of the target vehicle and relative coordinate information of the surrounding ground feature relative to the target vehicle, which are acquired by a vector camera; then, lane attribute information corresponding to an estimated position of the target vehicle in a high-precision map can be acquired, wherein the lane attribute information comprises at least one of lane width information and lane position information, and the lane position information is used for indicating whether a target lane is adjacent to a road boundary; then, judging whether a preset positioning accuracy adjustment condition is met according to the lane attribute information to obtain a first judgment result; and if the first judgment result shows that the preset positioning accuracy adjustment condition is met, adjusting the accuracy factor of the satellite positioning information of the target vehicle according to a positioning accuracy adjustment strategy corresponding to the preset positioning accuracy adjustment condition.

Optionally, the determining, according to the lane attribute information, whether a preset positioning accuracy adjustment condition is met or not to obtain a first determination result may specifically include: judging whether: the lane width of the lane where the target vehicle is located is smaller than a first preset threshold value, and the precision factor of the satellite positioning information of the target vehicle is larger than a second preset threshold value, so that a second judgment result is obtained. Correspondingly, if the first determination result indicates that the preset positioning accuracy adjustment condition is met, adjusting the accuracy factor of the satellite positioning information of the target vehicle according to a positioning accuracy adjustment strategy corresponding to the preset positioning accuracy adjustment condition may specifically include: and if the second judgment result shows that the lane width of the lane where the target vehicle is located is smaller than a first preset threshold value and the precision factor of the satellite positioning information of the target vehicle is larger than a second preset threshold value, reducing the precision factor of the satellite positioning information of the target vehicle according to a preset precision factor reduction scheme.

For example, RTKs give relatively accurate position information when the vehicle is traveling on a road that is only 4 meters wide, but for external reasons the position accuracy data values of the RTK signals are large, exceeding 4 meters. In such a case, the accuracy factor can be corrected by the lane information of the high-accuracy map, and the accuracy factor can be reasonably reduced.

Optionally, the determining, according to the lane attribute information, whether a preset positioning accuracy adjustment condition is met or not to obtain a first determination result may specifically include: judging whether: and the lane where the target vehicle is located is adjacent to the road boundary, and the precision factor of the satellite positioning information of the target vehicle is smaller than a third preset threshold value, so that a third judgment result is obtained. Correspondingly, if the first determination result indicates that the preset positioning accuracy adjustment condition is met, adjusting the accuracy factor of the satellite positioning information of the target vehicle according to a positioning accuracy adjustment strategy corresponding to the preset positioning accuracy adjustment condition may specifically include: and if the third judgment result indicates that the lane where the target vehicle is located is adjacent to the road boundary and the precision factor of the satellite positioning information of the target vehicle is smaller than a third preset threshold, amplifying the precision factor of the satellite positioning information of the target vehicle according to a preset precision factor amplifying scheme.

For example, when a vehicle is normally traveling in a lane adjacent to a road boundary, the RTK signal may deviate from the lane, but at this time, the RTK signal gives a small value of accuracy data, and the vehicle is considered to be located outside the road boundary. In such a case, the accuracy factor can be corrected by the lane information of the high-accuracy map, and the accuracy factor can be reasonably enlarged.

It should be understood that the above-mentioned example of correcting satellite positioning information (including positioning data itself and its accuracy factor) is only for illustration, and does not limit the application scope of the technical solution of the present disclosure, and other examples are also possible in practical application.

In one or more embodiments, the inertial measurement information collected by the inertial measurement unit may be corrected prior to data fusion positioning using the inertial measurement information.

Inertial measurement devices, also known as inertial measurement units or inertial sensors, are mainly used to detect and measure acceleration and rotational motion. The most basic inertial sensors include accelerometers and angular velocity meters (gyroscopes). The error of the inertial sensor increases with time, so that the inertial sensor can only be relied upon for positioning in a short period of time. Are commonly used in conjunction with GNSS (global navigation satellite system) in autonomous vehicles, known as combined inertial navigation.

Specifically, first, inertial measurement information of the target vehicle acquired by an inertial measurement unit may be acquired, the inertial measurement information may include acceleration and angular velocity; moreover, road attribute information corresponding to the estimated position of the target vehicle in the high-precision map can be obtained; then, the inertial measurement information range of the target vehicle can be determined according to the road attribute information; and correcting the inertial measurement information acquired by the inertial measurement unit according to the inertial measurement information range to obtain corrected inertial measurement information.

The road attribute information may specifically include road gradient information. That is, the obtaining, from the high-precision map data, the first map information corresponding to the estimated position of the target vehicle and the second map information within the preset distance range around the estimated position may specifically include: and acquiring road gradient information of a road to which the estimated position of the target vehicle in the high-precision map belongs. The correcting the sensor information acquired by the different kinds of sensors based on at least one of the first map information and the second map information to obtain corrected sensor information may specifically include: determining a theoretical acceleration range of the target vehicle based on the road gradient information; and correcting the acceleration acquired by the inertial measurement unit according to the theoretical acceleration range.

For example, due to external factors such as road surface fluctuation, the motion state of the vehicle is changed drastically, at this time, the IMU swings drastically along with the vehicle body, and the acceleration information of the IMU is changed drastically, so that the predicted speed and position of the vehicle are seriously suddenly changed.

It should be understood that the above examples given for correcting IMU data are only for illustration, and do not limit the application scope of the technical solution of the present disclosure, and other examples may also be used in practical applications. For example, the angular velocity information of the vehicle may also be corrected based on road attribute information (e.g., road curvature, etc.) of the position where the vehicle is located.

In one or more embodiments, the vehicle speed information collected by the vehicle speed detection device may be corrected before being subjected to data fusion positioning.

Specifically, first, vehicle speed information of the target vehicle acquired by a vehicle speed sensor may be acquired; and, road attribute information corresponding to an estimated position of the target vehicle in a high-precision map may be obtained, where the road attribute information includes at least one of road speed limit information, road curvature information, and road gradient information; then, a theoretical speed range of the target vehicle can be determined based on the road attribute information; and correcting the speed information of the target vehicle according to the theoretical speed range to obtain corrected speed information, or determining the precision factor of the speed information of the target vehicle.

For example, in an automatic driving environment, when the vehicle speed obtained by the vehicle speed sensor is 125km/h and the speed limit information of the road on which the vehicle is currently located is 60km/h to 120km/h from the high-precision map, the vehicle speed of 125km/h may be corrected (e.g., adjusted to 120km/h and marked) based on the limit of 60km/h to 120km/h, or the precision factor of the vehicle speed may be determined to be 5km/h or more.

In practice, the vehicle speed signal can only provide one item of data of the vehicle speed value, and cannot provide precision related information about the value. Under the condition of a high-precision map, the speed precision can be reasonably estimated and restrained according to speed limit information, road curvature, gradient and other information, so as to achieve a better positioning effect.

In one or more embodiments, the synchronous positioning and mapping information acquired by the odometer sensor may be corrected prior to data fusion positioning.

At present, solutions for vehicle positioning based on visual SLAM and laser SLAM (simultaneouslocalisation mapping) do not closely relate to a high-precision map, and the advantages of the high-precision map are not fully exerted.

In the embodiment of the present specification, specifically, first, synchronous positioning and mapping information acquired by an odometer sensor may be acquired, where the odometer sensor includes at least one of a visual odometer and a radar odometer, and the synchronous positioning and mapping information includes relative position information of a surrounding feature of the target vehicle with respect to the target vehicle; then, candidate object information of the target vehicle in a preset distance range around an estimated position in a high-precision map can be obtained, and the estimated position is determined based on satellite positioning information of the target vehicle; determining actual ground object information contained in the sensing information acquired by the odometer sensor by referring to the alternative ground object information; determining a target ground object corresponding to the actual ground object from the alternative ground objects; then, the vehicle pose information of the target vehicle can be corrected based on the absolute coordinate information of the target ground object and the relative coordinate information of the target ground object relative to the target vehicle; the vehicle pose information is determined based on synchronous positioning and mapping information acquired by the odometer sensor.

In practical application, the radar or visual perception information can be utilized to carry out semantic segmentation and target extraction under the assistance of the high-precision map, a perceived vector result (such as a label, a lane line and the like) is extracted, and the perceived vector result is matched with vector information stored in the high-precision map; after the matching is successful, the pose of the SLAM scheme is constrained by pose deviation between the pose of the stored quantity information and the perceived pose of the quantity data in the high-precision map, so that the high-precision positioning state of the vehicle is maintained.

In one or more embodiments, the data collected by any sensor may be corrected based on high-precision map data prior to data fusion positioning using the data collected by the sensor. Therefore, the accuracy of the fusion positioning result and the robustness of the vehicle navigation positioning can be improved.

In the embodiment of the specification, the method aims at providing high-precision pose information for automatic driving and intelligent cabins, combining the input of various sensors, dynamically fusing the information of a high-precision map in real time, and still maintaining high-precision positioning by means of the data information of the high-precision map under the condition that the high-precision positioning cannot be realized only by means of a positioning scheme of the sensors; when the confidence of the sensor data is unreliable, the confidence of the sensor is corrected by using a high-precision map.

The high-precision positioning of the scheme can enlarge the available range of the automatic driving function, and meanwhile, pose information highly matched with a high-precision map is easier to automatically drive and use an intelligent cabin, so that better car control effect and cabin display effect are achieved.

Based on the same thought, the embodiment of the specification also provides a device corresponding to the method. Fig. 3 is a schematic structural diagram of a fusion positioning module corresponding to fig. 2 according to an embodiment of the present disclosure. As shown in fig. 3, the module may include:

a sensor information acquisition unit 302 for acquiring sensor information acquired by a plurality of different kinds of sensors mounted on a target vehicle, respectively;

a high-precision map information obtaining unit 304, configured to obtain, from high-precision map data, first map information corresponding to a predicted position of the target vehicle and second map information within a preset distance range around the predicted position; the first map information includes at least one of lane attribute information and road attribute information; the second map information comprises ground object attribute information;

a correction unit 306, configured to correct the sensor information acquired by the different types of sensors based on at least one of the first map information and the second map information, so as to obtain corrected sensor information;

And a data fusion unit 308, configured to fuse the corrected sensor information corresponding to the plurality of different types of sensors with the high-precision map data, so as to obtain fused high-precision positioning information of the target vehicle.

It will be appreciated that each of the modules described above refers to a computer program or program segment for performing one or more particular functions. Furthermore, the distinction of the above-described modules does not represent that the actual program code must also be separate.

Based on the same thought, the embodiment of the specification also provides a high-precision map engine corresponding to the method and the module.

Fig. 4 is a schematic structural diagram of a high-precision map engine according to an embodiment of the present disclosure.

As shown in fig. 4, the high-precision map engine 400 may include:

comprising a fusion positioning module 401 as shown in fig. 3;

the electronic horizon module 402 is configured to receive external high-definition vehicle position information and match the external high-definition vehicle position information to a map, and provide a functional interface for an autopilot application to perform rule judgment;

and at least one of an autopilot design run domain judgment module 403, a map update module 404, a crowd-sourced preprocessing and backhaul module 405, a path cross-correlation module 406, and a lane-level path planning module 407.

Wherein, the autopilot design operation domain judging module 403 is configured to configure an autopilot area and judge a requirement;

the map updating module 404 is configured to obtain map data updating information of the high-precision map based on the vehicle position and the planned path;

the crowd-sourced preprocessing and returning module 405 is configured to return the cloud and update the map data center by preprocessing such as screening and fusing the UGC visual vector data;

the path cross-correlation module 406 is configured to synchronize a global path planning result initiated by a user to an autopilot system, and obtain a matching path of a navigation path on a high-precision map by cross-correlating with the high-precision map;

the lane-level path planning module 407 is configured to output a lane level and a local path plan within a certain length range in front of the vehicle according to the results of the navigation path matching and the route correction.

The foregoing describes particular embodiments of the present disclosure, and in some cases, acts or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are referred to each other.

The apparatus, the device, and the method provided in the embodiments of the present disclosure correspond to each other, and therefore, the apparatus, the device, and the method also have similar beneficial technical effects as those of the corresponding method, and since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, device are not described here again.

In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (ProgrammableLogicDevice, PLD), such as a field programmable gate array (FieldProgrammableGateArray, FPGA), is an integrated circuit whose logic function is determined by the programming of the device by a user. The designer programs itself to "integrate" a digital system onto a single PLD without requiring the chip manufacturer to design and fabricate application specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called Hardware description language (Hardware DescriptionLanguage, HDL), but HDL is not only one but a plurality of kinds, such as ABEL (Advanced BooleanExpressionLanguage), AHDL (AlteraHardwareDescriptionLanguage), confluence, CUPL (cornelluniversity program language), HDCal, JHDL (JavaHardwareDescription Language), lava, lola, myHDL, PALASM, RHDL (rubyhardware description language), and so on, VHDL (Very-High-SpeedIntegratedCircuitHardwareDescription Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (ApplicationSpecificIntegratedCircuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmelAT91SAM, microchipPIC F26K20 and silicane labsc8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.

The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present application.

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash memory (flashRAM). Memory is an example of computer-readable media.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transshipment) such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.

Claims (14)

1. A method for determining high-precision positioning information, the method comprising:

respectively acquiring sensor information acquired by a plurality of different types of sensors loaded on a target vehicle;

acquiring first map information corresponding to the estimated position of the target vehicle and second map information within a preset distance range around the estimated position from high-precision map data; the first map information includes at least one of lane attribute information and road attribute information; the second map information comprises ground object attribute information;

correcting the sensor information acquired by the different types of sensors based on at least one of the first map information and the second map information to obtain corrected sensor information;

and fusing the corrected sensor information corresponding to the plurality of different types of sensors with the high-precision map data to obtain fused high-precision positioning information of the target vehicle.

2. The method of claim 1, wherein the correcting the sensor information acquired by the different kinds of sensors based on at least one of the first map information and the second map information, respectively, specifically includes:

the sensor information includes sensing data;

the sensor data in the sensor information is corrected based on at least one of the first map information and the second map information.

3. The method of claim 2, wherein the sensed data specifically includes at least one of the following: satellite positioning data, inertial measurement data, radar mileage data, visual mileage data, or vehicle speed data.

4. The method of claim 1, wherein the correcting the sensor information acquired by the different kinds of sensors based on at least one of the first map information and the second map information, respectively, specifically includes:

the sensor information includes a precision factor;

and correcting the precision factor in the sensor information based on the first map information.

5. The method of claim 2, wherein the sensed data includes satellite positioning information of the target vehicle acquired by a satellite positioning device;

The correcting the sensing data in the sensor information based on at least one of the first map information and the second map information specifically includes:

determining a target ground object matched with the surrounding ground object acquired by the vector camera from the candidate ground objects in a preset distance range around the estimated position of the target vehicle in the high-precision map; the estimated position is determined based on satellite positioning information of the target vehicle;

and correcting satellite positioning information of the target vehicle based on the absolute coordinate information of the target ground object and the relative coordinate information of the target ground object relative to the target vehicle.

6. The method of claim 4, wherein the lane attribute information includes at least one of lane width information and lane position information;

the correcting the precision factor in the sensor information based on the first map information specifically includes:

judging whether a preset positioning accuracy adjustment condition is met or not according to the lane attribute information to obtain a first judgment result;

and if the first judgment result shows that the preset positioning accuracy adjustment condition is met, adjusting the accuracy factor of the satellite positioning information of the target vehicle according to a positioning accuracy adjustment strategy corresponding to the preset positioning accuracy adjustment condition.

7. The method of claim 6, wherein the determining whether a preset positioning accuracy adjustment condition is satisfied according to the lane attribute information, specifically comprises:

judging whether the lane width of the lane where the target vehicle is located is smaller than a first preset threshold value, and the precision factor of the satellite positioning information of the target vehicle is larger than a second preset threshold value;

if the first determination result indicates that a preset positioning accuracy adjustment condition is met, adjusting a precision factor of satellite positioning information of the target vehicle according to a positioning accuracy adjustment strategy corresponding to the preset positioning accuracy adjustment condition, wherein the method specifically comprises the following steps:

if the lane width of the lane where the target vehicle is located is smaller than a first preset threshold value and the precision factor of the satellite positioning information of the target vehicle is larger than a second preset threshold value, the precision factor of the satellite positioning information of the target vehicle is reduced according to a preset precision factor reduction scheme.

8. The method of claim 6, wherein the lane position information is specifically first information indicating that the target lane is adjacent to the road boundary or second information indicating that the target lane is not adjacent to the road boundary;

Judging whether a preset positioning accuracy adjustment condition is met or not according to the lane attribute information, specifically comprising:

judging whether the lane where the target vehicle is located is adjacent to a road boundary, and judging whether the precision factor of satellite positioning information of the target vehicle is smaller than a third preset threshold;

if the first determination result indicates that a preset positioning accuracy adjustment condition is met, adjusting a precision factor of satellite positioning information of the target vehicle according to a positioning accuracy adjustment strategy corresponding to the preset positioning accuracy adjustment condition, wherein the method specifically comprises the following steps:

and if the lane where the target vehicle is located is adjacent to the road boundary and the precision factor of the satellite positioning information of the target vehicle is smaller than a third preset threshold, amplifying the precision factor of the satellite positioning information of the target vehicle according to a preset precision factor amplifying scheme.

9. The method of claim 2, wherein the sensed data includes inertial measurement information of the target vehicle acquired by an inertial measurement unit; the inertial measurement information includes acceleration and angular velocity;

the correcting the sensing data in the sensor information based on at least one of the first map information and the second map information specifically includes:

Determining an inertial measurement information range of the target vehicle according to the road attribute information;

and correcting the inertial measurement information acquired by the inertial measurement unit according to the inertial measurement information range to obtain corrected inertial measurement information.

10. The method of claim 9, wherein the road attribute information specifically includes road grade information;

the correcting the sensing data in the sensor information based on at least one of the first map information and the second map information specifically includes:

determining a theoretical acceleration range of the target vehicle based on the road gradient information;

and correcting the acceleration acquired by the inertial measurement unit according to the theoretical acceleration range.

11. The method of claim 2, wherein the sensed data includes vehicle speed information of the target vehicle collected by a vehicle speed sensor; the road attribute information includes at least one of road speed limit information, road curvature information, and road gradient information;

the correcting the sensing data in the sensor information based on at least one of the first map information and the second map information specifically includes:

Determining a theoretical speed range of the target vehicle based on the road attribute information;

and correcting the speed information of the target vehicle according to the theoretical speed range to obtain corrected speed information, or determining the precision factor of the speed information of the target vehicle.

12. The method of claim 2, wherein the sensing data comprises synchronized positioning and mapping information collected by an odometer sensor; the odometer sensor includes at least one of a visual odometer or a radar odometer; the synchronous positioning and mapping information comprises relative position information of surrounding features of the target vehicle relative to the target vehicle;

the correcting the sensing data in the sensor information based on at least one of the first map information and the second map information specifically includes:

determining actual ground object information contained in sensing information acquired by the odometer sensor by referring to the candidate ground objects in a preset distance range around an estimated position of the target vehicle in a high-precision map; the estimated position is determined based on satellite positioning information of the target vehicle;

Determining a target ground object corresponding to the actual ground object from the alternative ground objects;

correcting vehicle pose information of the target vehicle based on absolute coordinate information of the target ground object and relative coordinate information of the target ground object relative to the target vehicle; the vehicle pose information is determined based on synchronous positioning and mapping information acquired by the odometer sensor.

13. A fusion positioning module, the module comprising:

a sensor information acquisition unit configured to acquire sensor information acquired by a plurality of different types of sensors mounted on a target vehicle, respectively;

a high-precision map information acquisition unit, configured to acquire, from high-precision map data, first map information corresponding to an estimated position of the target vehicle and second map information within a preset distance range around the estimated position; the first map information includes at least one of lane attribute information and road attribute information; the second map information comprises ground object attribute information;

the correction unit is used for respectively correcting the sensor information acquired by the different types of sensors based on at least one of the first map information and the second map information to obtain corrected sensor information;

And the data fusion unit is used for fusing the corrected sensor information corresponding to the plurality of different types of sensors with the high-precision map data to obtain fused high-precision positioning information of the target vehicle.

14. A high precision map engine, comprising:

the fusion locator module of claim 13;

the electronic horizon module is used for receiving external high-precision vehicle position information and matching the external high-precision vehicle position information with a map, and providing a functional interface for automatic driving application to conduct regulation and judgment;

and at least one of an automatic driving design operation domain judging module, a map updating module, a crowdsourcing preprocessing and returning module, a path cross correlation module and a lane-level path planning module;

the automatic driving design operation domain judging module is used for configuring an automatic driving area and judging requirements;

the map updating module is used for obtaining map data updating information of the high-precision map based on the vehicle position and the planned path;

the crowd-sourced preprocessing and returning module is used for returning the cloud and updating the map data center by preprocessing such as screening, fusing and the like on UGC visual vector data;

The route cross-correlation module is used for synchronizing a global route planning result initiated by a user to the automatic driving system, and obtaining a matching route of the navigation route on the high-precision map through cross-correlation with the high-precision map;

the lane-level path planning module is used for outputting lane levels and local path planning within a certain length range in front of the vehicle according to the navigation path matching and route correction results.

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