Geonavigation satellite system error modeling

Abstract

A vehicle includes a controller having a global navigation system satellite (GNSS) positioning module and a sensor fusion module. A plurality of vehicle sensors are connected to the controller. The sensor fusion module includes software configured to fuse sensor data from the plurality of vehicle sensors and the GNSS position by applying an error weight to each element of the data from the plurality of vehicle sensors and the GNSS position. The error weight of the GNSS position is variable depending on a GNSS error model map.

Description

Technical Field

[0001] This disclosure relates to vehicles, and more particularly to the weighting of a geographic navigation satellite system (GNSS) positioning within a sensor fusion system to be assigned to a vehicle. Background Technology

[0002] Vehicles with built-in autonomous and / or semi-autonomous navigation systems utilize multiple sources of sensor information. This sensor information provides the vehicle's fixed location, relative location information between the vehicle and surrounding elements, and information relevant to the vehicle's operation. This sensor information is then used by various vehicle systems to aid in vehicle operation.

[0003] In some cases, one or more sensor types located on and communicating with a vehicle may provide conflicting values ​​for the same data point. For example, a GNSS system may indicate that a vehicle is in one location; however, map data and sensor data may indicate different locations. To account for these differences and the varying error tolerances between different sensors, sensor fusion systems combine sensor outputs and apply a weighted average to each similar data point.

[0004] Accordingly, it is desirable to provide a system for determining the desired accuracy of GNSS location data points and to assign weights to GNSS locations corresponding to the desired accuracy. Summary of the Invention

[0005] In one exemplary embodiment, a vehicle includes a controller having a Global Navigation System Satellite (GNSS) positioning module and a sensor fusion module. A plurality of vehicle sensors are connected to the controller. The sensor fusion module includes software configured to fuse sensor data from the plurality of vehicle sensors and the GNSS location by applying error weights to each element of data from the plurality of vehicle sensors and the GNSS location. The error weights of the GNSS location are variable and depend on a GNSS error model map.

[0006] In addition to one or more of the features described herein, the GNSS error model map is divided into multiple spatial regions, each of which has a corresponding expected GNSS error.

[0007] In addition to one or more of the features described herein, the corresponding expected GNSS error takes into account at least one of GNSS signal jamming and GNSS multipath error.

[0008] In addition to one or more of the features described herein, the corresponding expected GNSS error is based on the change between the relative position of the vehicle determined via the plurality of vehicle sensors and the GNSS position of the vehicle determined by the GNSS positioning module.

[0009] In addition to one or more of the features described herein, the relative position of the vehicle is determined by comparing the outputs of the plurality of vehicle sensors with a point cloud map of the area in which the vehicle operates.

[0010] In addition to one or more of the features described herein, the relative position of the vehicle is determined by comparing the outputs of the plurality of vehicle sensors with a semantic map of the area in which the vehicle operates.

[0011] In addition to one or more of the features described herein, the expected GNSS error for each space region is based on the variance of the differences between observations within the space region.

[0012] In addition to one or more of the features described herein, the expected GNSS error for each space region is interpolated across multiple observation points within the space region.

[0013] In addition to one or more of the features described herein, the interpolation is at least one of spline-based interpolation, kriging-based interpolation, nearest neighbor-based interpolation, and natural neighbor-based interpolation.

[0014] In addition to one or more of the features described herein, the GNSS error model map is derived from multiple modes of transportation.

[0015] In another exemplary embodiment, a method for fusing sensor data from a vehicle includes applying error weights to each element of data from multiple vehicle sensors and GNSS locations. The error weights for the GNSS locations are variable depending on the GNSS error model map and the vehicle's location.

[0016] In yet another exemplary embodiment, the GNSS error model map is divided into multiple spatial regions, and each spatial region has a corresponding expected GNSS error.

[0017] In yet another exemplary embodiment, the corresponding expected GNSS error takes into account at least one of GNSS signal blocking and GNSS multipath error.

[0018] In yet another exemplary embodiment, the corresponding expected GNSS error is based on the change between the relative position of the vehicle determined by the plurality of vehicle sensors and the GNSS position of the vehicle determined by the GNSS positioning module.

[0019] In yet another exemplary embodiment, the relative position of the vehicle is determined by comparing the outputs of the plurality of vehicle sensors with a point cloud map of the area in which the vehicle operates.

[0020] In yet another exemplary embodiment, the relative position of the vehicle is determined by comparing the outputs of the plurality of vehicle sensors with a semantic map of the area in which the vehicle operates.

[0021] In yet another exemplary embodiment, the expected GNSS error for each space region is based on the variance of the difference between observation points within the space region.

[0022] In yet another exemplary embodiment, the expected GNSS error for each space region is interpolated across multiple observation points within the space region.

[0023] In yet another exemplary embodiment, the interpolation is at least one of spline-based interpolation, kriging-based interpolation, nearest neighbor-based interpolation, and natural neighbor-based interpolation.

[0024] In yet another exemplary embodiment, the GNSS error model map is derived from multiple vehicles.

[0025] The above features and advantages, as well as other features and advantages of this disclosure, will become apparent from the following detailed description taken in conjunction with the accompanying drawings. Attached Figure Description

[0026] Other features, advantages, and details appear by way of example only in the following detailed description, which refers to the accompanying drawings, in which: Figure 1 It is a vehicle that includes a controller and multiple sensor systems; Figure 2 It is aimed at Figure 1 Sensor fusion architecture for vehicles; Figure 3 This is an illustration of potential Global Navigation Satellite System (GNSS) errors caused by urban canyon environments; Figure 4 This is the general process for generating GNSS error maps; Figure 5 The diagram shows... Figure 4 The first embodiment of the general process; Figure 6 Is Figure 5 The point cloud map generated during the process; Figure 7 Demonstrated the use of Figure 6 Drawing a relative local location map of the point cloud; Figure 8 The diagram shows... Figure 7 The spatial region within the point cloud data; Figure 9 The diagram shows... Figure 4 The second embodiment of the general process; Figure 10 The illustration shows that Figure 9 Semantic map data generated during the process; and Figure 11 The diagram shows... Figure 10 Drawing relative local location maps from semantic map data. Detailed Implementation

[0027] The following description is exemplary in nature only and is not intended to limit this disclosure, its application, or use. It should be understood that, throughout the accompanying drawings, corresponding reference numerals indicate similar or corresponding parts and features. As used herein, the term "module" refers to processing circuitry that may include application-specific integrated circuits (ASICs), electronic circuitry, processors (shared, dedicated, or grouped), and memory that executes one or more software or firmware programs, combinational logic circuitry, and / or other suitable components that provide the described functionality.

[0028] As used herein, the term “controller” refers to a dedicated processor and memory, a distributed control architecture comprising multiple dedicated processors communicating with each other, a remote processing system communicating with a local pass-through processor, or any similar processing architecture capable of exerting control in the manner described.

[0029] According to an exemplary embodiment, a platoon of vehicles provides distributed sensor data and Global Navigation Satellite System (GNSS) position data to a central system. The central system uses a local map to determine the position (referred to as relative position) of each vehicle in the platoon relative to the local map. Simultaneously, the relative position is associated with the GNSS position determined by the GNSS. Based on the difference between the relative position and the associated GNSS position, an error model identifying the probability of GNSS position error is established at the central system. The central system divides the map into spatial regions based on the expected GNSS error determined by the error model. The combined set of spatial regions is called an error map and is distributed from the central system to one or more vehicles.

[0030] In an alternative embodiment, the GNSS error map can be generated by a controller that is entirely local to the vehicle, using GNSS positions and relative positions generated by the vehicle passing through the same map area multiple times.

[0031] In a further alternative embodiment, the GNSS error map can be generated by a controller located locally on the vehicle using a dataset of relative positions and associated GNSS positions received from a central system.

[0032] Turning to a detailed explanation of some embodiments, Figure 1 The illustration shows a vehicle 10, including a main body 12 and a passenger compartment 14. A controller 20 is located within the vehicle 10. The controller 20 includes a communication system 22 that communicates collectively with a central computing system 30 and GNSS satellites 40. In some examples, the communication system comprises multiple receivers and transmitters configured to communicate with different external systems. Additionally, the controller 20 communicates with multiple vehicle sensors 50 configured to provide relative positioning information related to the vehicle. A sensor fusion module 24 and a GNSS error map module 26 are included within the controller 20.

[0033] The vehicle sensor 50 may include an inertial motion unit, a wheel odometer, a camera, a light sensor, and any number of additional sensors and sensor types capable of contributing to the determination of the relative position of the vehicle 10. The vehicle 10 may be part of a platoon of similar vehicles, wherein each vehicle 10 in the platoon includes similar sensing, positioning, and processing capabilities.

[0034] Continue to refer to Figure 1 , Figure 2 An example architecture of the sensor fusion module 24 is illustrated. The sensor fusion module 24 receives data from multiple sources, including GNSS positioning from GNSS satellite 40 and sensor outputs from each of the vehicle's sensors 50, and combines the data from these multiple sources into a single location for the vehicle. This location is then provided to one or more downstream modules 60, such as autonomous or semi-autonomous vehicle operation modules.

[0035] In order to properly synthesize potentially inconsistent data from multiple sources of data (e.g., different vehicle locations), sensor fusion module 24 applies weights to each data source for a given information fragment (e.g., each data source providing vehicle location information), where a heavier weight corresponds to a higher accuracy expected from that particular source.

[0036] Sources such as vehicle sensor 50 have generally consistent accuracy, and the specific weights of data from this source are fixed in sensor fusion module 24. However, the accuracy of GNSS location data varies substantially based on multiple factors, including orbital errors, satellite clock errors, ionospheric delay, tropospheric delay, multipath and signal jamming, and receiver noise. Existing systems for estimating GNSS accuracy typically focus on taking into account orbital errors, satellite clock errors, ionospheric delay, and tropospheric delay, because the number and magnitude of multipath and signal jamming errors are highly location-dependent.

[0037] Continue to refer to Figure 1 and 2 , Figure 3 The illustration shows vehicle 10 continuing through an urban canyon environment 302. An urban canyon environment is a place where streets 306 are flanked by buildings 308, creating a canyon-like environment. GNSS operates by timing the travel time of signals from satellites 40 with known orbital positions to vehicle 10. When the signal is direct and unobstructed (e.g., a central signal 310), the precise position of vehicle 10 relative to the satellite 40 that initiated the signal 310 can be determined. Using at least three such signals, the absolute geospatial position of vehicle 10 can be calculated with accuracy to the GNSS positioning system.

[0038] When building 308 blocks the signal (e.g., signal 320), a signal blocking error occurs, and the reduced number of available GNSS signals leads to a decrease in the accuracy of the determined location.

[0039] Similarly, when signal 330 is reflected away from building 308 or other objects and then received by vehicle 10, the travel time of signal 330 is artificially increased, and the accuracy of the determined location is reduced.

[0040] To address the reduced accuracy caused by blocked signal 320 and multipath signal 330, sensor data from vehicle sensor 50 is provided to central computing system 30 to create a local map of the location. The local map includes landmarks (e.g., lane lines, road signs, images of buildings, trees, and other fixed structures) and vehicle trajectories. For any given pair of data points (e.g., a pair of vehicles 10 passing through an area or multiple passes through the same area by a single vehicle 10), the relative position of the vehicle 10 can be calculated based on the local map and using GNSS positioning. By assuming the accuracy of the local map, a geographic GNSS error model is created based on the difference between the relative position on the local map and the relative position derived from GNSS measurements.

[0041] Continue to refer to Figure 1-3 , Figure 4 The diagram illustrates a process 400 for generating a local error map. Initially, it includes... Figure 1 The vehicle 10 uses vehicle sensors 50 and local maps, along with GNSS positioning, to generate paired location points. These paired location points are then provided to the central computing system 30 in the first step 410.

[0042] Once the data has been provided to the central computing system 30, a new local map is created in the local map creation step 420 based on location-specific data from the vehicle sensors 50. In some examples, a high-resolution (HD) map of the area may already exist and be available. In this case, the vehicle sensor data 50 is applied to the existing HD map to further improve the local map, rather than creating a new map.

[0043] The local map includes reference images and sensor data, which allows for precise positioning of the vehicle 10 in step 430 of calculating relative position using the local map by comparing the positions of the images and sensor data from the vehicle sensor 50 with those of the reference images and sensor data.

[0044] While using a local map to determine the relative position, process 400 calculates the GNSS position based on the GNSS measurement results in step 440 of calculating the GNSS position.

[0045] After creating both sets of locations in steps 430 and 440, the central computing system 30 divides the local map into spatial regions in the map partitioning step 450. Spatial regions can be uniformly distributed geographic blocks, traffic blocks, urban districts, or any other existing division of the local map.

[0046] In step 460 of creating the GNSS error model, the GNSS error model is calculated for each spatial region of the local map, and the GNSS error model is distributed to each vehicle 10 configured to utilize the local map.

[0047] Continue to refer to Figure 1-4 , Figure 5-8 The illustration shows the execution of a high-precision point cloud map generated by the vehicle sensor 50. Figure 4 Process 400, process 500, among which Figure 5 The flowchart of the process is shown. Figure 6 The diagram illustrates the point cloud map 602 generated by steps 510 and 520 of the process. Figure 7 The diagram illustrates the calculation of the relative position offset in steps 530 and 540, and... Figure 8 The diagram illustrates the division of the point cloud and local map 602 into spatial regions 810 and 820.

[0048] Initially, in step 510 of creating the point cloud map, the original image and a Simultaneous Localization and Mapping (SLAM) algorithm are used to create a point cloud map 602 aligned with a real-world coordinate system (such as latitude and longitude). As an example, the SLAM algorithm may include OrbSLAM, VinsFusion, Structure from Motion (SFM), or any similar SLAM algorithm. Map 602 includes at least two vehicles 604 and 606, and defines vehicle trajectories 610 (corresponding to vehicle 604) and 612 (corresponding to vehicle 606). Map 602 further includes multiple reference points 614 and vehicle positions 616 and 618 along the corresponding trajectories 610 and 612.

[0049] In the alignment step 520, process 500 aligns the point cloud map 602 with the world coordinate system, and in the position calculation step 530, it uses the point cloud map 602 and in the position calculation step 540, it uses GNSS measurement results to calculate the relative positions of vehicles 604 and 606. The relative position is defined by an offset 702 between a vehicle's position and nearby vehicle positions, such that for any given point P... i,j The given point has a relative position with respect to its neighboring points, which can be P. i,j -P l,m And for any given point P i,j Corresponding GNSS position G i,j The relative position of adjacent GNSS positions is G. i,j -G l,m .

[0050] After determining the relative positions, in step 550 of dividing the local map into spatial regions, the point cloud map 602 is divided into... Figure 8 Space zones 810 and 820.

[0051] After dividing the map into spatial regions, in step 560, which creates a GNSS error model for each region, the differences in relative positions from steps 530 and 540 are used to calculate the GNSS error within that region. In one example, the GNSS error model is calculated according to the following formula: discrepency (i，j)-(l，m) =(G i，j -G l，m )-(P i，j -P l，m ) discrepency_variance=Variance i，l j，m (discrepency (i，j)-(l，m) ) The difference is the variation between each relative position and the corresponding GNSS position, the difference variance is the variation of the difference across spatial regions 810 and 820, and the GNSS error variance is the error value assigned to regions 810 and 820 in the GNSS error model.

[0052] Refer again Figure 1-4 , Figure 9-11 The diagram illustrates the execution using a semantic-based local map. Figure 4 Process 400 is followed by process 900. Initially, process 900 receives crowdsourced local data from vehicle 10 and creates a new semantic map 1002 in step 910 of creating a semantic map. Figure 10 (See illustration in the middle). The semantic map can be created using any existing semantic map algorithm and tracks the semantic features of the road on which the vehicle 10 travels, such as lane lines 1004, 1008, curbs 1006, 1010, etc. Figure 10 In the example, the first vehicle 1020 tracks a first set of semantic features (lane lines 1004 and curb 1006), and the second vehicle 1022 tracks a second set of semantic features (lane lines 1008 and curb 1010).

[0053] In the presence of an existing HD map created using semantic features, the existing map can be utilized by combining new data from the vehicle sensor 50 to create an updated local map.

[0054] In step 912, which uses a semantic map to calculate relative position, the relative positions of vehicles 1020 and 1022 are determined based on the semantic map 1002, and in step 914, which uses GNSS measurement results to calculate position, GNSS signals are used to determine GNSS position.

[0055] After identifying both relative position and GNSS position, the semantic map 1002 is divided into segments in step 916, which involves dividing the local map into spatial regions. Figure 11 The space regions are 1102, 1104, and 1106, and a GNSS error model is created for each space region in step 918 of creating the GNSS error model.

[0056] exist Figure 11 The calculation of the GNSS error model for an example point is illustrated in spatial region 1104. For a given point P... i,j Through P i,j -P l,m To calculate the relationship between adjacent points P l,m The cross-trajectory offset. Then, for point P i,j via G i,j -G l,m To calculate the relationship between adjacent points Pl,m The corresponding GNSS offsets. Using these reference points, the GNSS error model is: discrepency (i，j)-(l，m) =(G i，f -G l，m )-(P i，j -P l，m ) discrepency_variance=Variance i，l j，m (discrepency (i，j)-(l，m) ) The difference is the variation between each relative position and the corresponding GNSS position, the difference variance is the variation of the difference across spatial regions 810 and 820, and the GNSS error variance is the error value assigned to regions 810 and 820 in the GNSS error model.

[0057] Referring to both Process 500 and Process 900, considering the minimum threshold of granularity in the error measurement results, one or more algorithms can be used to interpolate the error metric between observation points, thereby providing a more complete GNSS error map. As an example, interpolation can take the form of one or more of the following: spline-based algorithms, kriging-based algorithms (or alternatively, Gaussian process regression), nearest neighbor-based algorithms, and / or natural neighbor-based algorithms. Interpolating observation points improves the output of the GNSS error map by incorporating weights given to error estimates from different observation points.

[0058] Despite Figure 4 and 9 The examples described and illustrated are separate, but it should be understood that they can be used... Figure 4 Point cloud-based processes 500 and Figure 9 The semantic map-based process combines the two to generate a GNSS error map.

[0059] Furthermore, although this article describes a city canyon environment, it should be understood that the system and features apply to environments in which vehicle 10 passes through areas where multipath and / or signal congestion positioning errors are likely to occur.

[0060] The terms “a” and “an” do not indicate a limitation of quantity, but rather the presence of at least one of the referenced items. The term “or” means “and / or” unless otherwise clearly indicated by the context. Throughout the specification, the reference to “aspect” means that a particular element (e.g., a feature, structure, step, or characteristic) described in conjunction with that aspect is included in at least one aspect described herein and may or may not be present in other aspects. Furthermore, it should be understood that the described elements may be combined in any suitable manner across various aspects.

[0061] When an element, such as a layer, film, region, or substrate, is referred to as being "on" another element, it can be directly on that other element, or an intermediary element may be present. In contrast, when an element is referred to as being "directly on" another element, no intermediary element is present.

[0062] Unless otherwise specified herein, all test standards are, in fact, the most recent standard up to the filing date of this application or, where priority is claimed, the filing date of the earliest priority application in which the test standard appears.

[0063] Unless otherwise defined, the technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0064] Although the above disclosure has been described with reference to exemplary embodiments, those skilled in the art will understand that various changes can be made and elements can be substituted with equivalents without departing from its scope. Furthermore, many modifications can be made to adapt particular situations or materials to the teachings of this disclosure without departing from its substantial scope. Therefore, it is intended that this disclosure should not be limited to the specific embodiments disclosed, but rather to include all embodiments falling within its scope.

Claims

1. A vehicle comprising: a controller having a global navigation satellite system (GNSS) positioning module and a sensor fusion module; a plurality of vehicle sensors connected to the controller; the sensor fusion module including software configured to fuse sensor data from the plurality of vehicle sensors and a GNSS position by applying an error weight to each element of data from the plurality of vehicle sensors and the GNSS position, and wherein the error weight of the GNSS position is variable depending on a GNSS error model map.

2. The vehicle of claim 1, wherein the GNSS error model map is divided into a plurality of spatial zones, and wherein each spatial zone has a corresponding expected GNSS error.

3. The vehicle of claim 2, wherein the corresponding expected GNSS error accounts for at least one of GNSS signal blockage and GNSS multipath error.

4. The vehicle of claim 2, wherein the corresponding expected GNSS error is based on a change between a relative position of the vehicle determined via the plurality of vehicle sensors and a GNSS position of the vehicle determined by the GNSS positioning module.

5. The vehicle of claim 4, wherein the relative position of the vehicle is determined via comparing outputs of the plurality of vehicle sensors to a point cloud map of a zone in which the vehicle is operating.

6. The vehicle of claim 4, wherein the relative position of the vehicle is determined via comparing outputs of the plurality of vehicle sensors to a semantic map of a zone in which the vehicle is operating.

7. The vehicle of claim 2, wherein the expected GNSS error of each spatial zone is based on a variance of difference of observation points within the spatial zone.

8. The vehicle of claim 7, wherein the expected GNSS error of each spatial zone is interpolated across a plurality of observation points within the spatial zone.

9. The vehicle of claim 8, wherein the interpolation is at least one of a spline-based interpolation, a Kriging-based interpolation, a nearest neighbor-based interpolation, and a natural neighbor-based interpolation.

10. The vehicle of claim 1, wherein the GNSS error model map is derived from a plurality of vehicles.

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