Systems and methods for augmenting GNSS with terrain-based clustering insights - Patents.com

Abstract

Methods and systems related to determining ordering and / or positioning of travel lanes in a road segment are disclosed. In some embodiments, this may include obtaining road surface profiles associated with the road segment and clustering them, for example, using similarity or other suitable metrics. The lateral offset or location of the clustered profiles may be used in determining the lane ordering and / or location. The resulting lane-specific information may be used to determine the vehicle's travel lane by comparing a current road profile obtained from the vehicle with road profile information associated with different lanes. In other embodiments, a method and / or system for augmenting Global Navigation Satellite System (GNSS) signals may include determining a lateral offset using raw GNSS signals and GNSS locations associated with terrain-based data for use in determining a corrected GNSS location of the vehicle.

Description

[Technical field]

[0001] Related Applications This application claims the benefit of priority under 35 U.S.C. Section 119(e) to U.S. Provisional Patent Application No. 63 / 296,531, filed January 5, 2022, and U.S. Provisional Patent Application No. 63 / 213,396, filed June 22, 2021, the disclosures of each of which are incorporated by reference in their entireties herein. Summary of the Invention [Means for solving the problem]

[0002] overview According to one aspect, the disclosure discusses a method for determining lane ordering in a road segment that may have multiple lanes. The method may include receiving a plurality of road profiles (e.g., road surface or sub-surface profiles), each road profile based on data collected during each of a plurality of traverses of the road segment, determining a representative lateral offset of each driving path as each road profile was collected during each traverse (e.g., by using a GNSS system), determining a degree of similarity between the plurality of road profiles, clustering the road profiles based on their degree of similarity, where profiles having a similarity greater than a preset threshold may be associated with the same driving lane, determining an average value of the representative lateral offset of the driving path associated with each road surface profile in each cluster, and determining a number of driving lanes (marked or unmarked) in the road segment, a lane ordering of the road segment, and / or a lateral offset of the multiple lanes or driving paths relative to a suitable reference line based on the average value of the representative lateral offset of the driving path associated with each road surface profile in each cluster. In some implementations, the method may also include determining a spacing between the calculated centerlines of at least two lanes of the road segment.

[0003] According to one aspect, the disclosure discusses a method for determining a lateral position of a drive path or a travel lane on a road segment. The method may include traversing a road segment by traveling along a plurality of drive paths on the road segment with one or more vehicles, determining a road surface profile and a lateral position of each of the drive paths, clustering the road surface profiles based on their similarity, identifying a first cluster including a sufficient number of road surface profiles, the sufficient number being equal to or greater than a preset threshold, determining a representative road surface profile for the first cluster, determining a representative lateral position of a set of drive paths corresponding to the road surface profiles in the first cluster, and determining a lateral position of the travel lane based on an average value of the representative lateral positions of the drive paths associated with the road surface profiles in the first cluster. In some implementations, the representative lateral position of a given drive path may be determined based on a single lateral position measurement or an average value of multiple lateral position measurements made (e.g., by using a GNSS receiver mounted on the vehicle) while traveling along the drive path. In some implementations, the lateral position of the travel lane may be equal to an average value of representative lateral positions of a set of driving paths corresponding to the road surface profiles in the first cluster. In some implementations, the lateral position of the given driving path is based on one or more GNSS measurements while traveling along the driving path. In some implementations, the number of lanes is equal to the number of clusters that include a sufficient number of road surface profiles. In some embodiments, the method may include determining a current lane of the vehicle based on matching the current road surface profile with previously determined representative road data associated with the travel lane. In some embodiments, the representative road surface profile data may be received from a remote data storage system (e.g., cloud storage).

[0004] According to one aspect, this disclosure discusses a method for determining lane ordering for a road segment. The method may include receiving information about characteristics of a subsurface structure beneath a plurality of driving paths, the characteristics based on data collected while driving across each of the driving paths of the road segment, determining a representative lateral offset for each of the driving paths of the road segment, determining a similarity between the received information about the subsurface characteristics of the plurality of driving paths, clustering the information, where clusters having a similarity greater than a preset threshold are associated with the same lane of the road segment, determining an average value of the representative lateral offset associated with each of the driving paths in each cluster, and determining an ordering of the lanes of the road segment based on the average value.

[0005] According to one aspect, the present disclosure provides a method for augmenting or correcting real-time GNSS signals corresponding to a current location of a vehicle, the method including: (a) receiving GNSS signals corresponding to a current location of the vehicle from a GNSS sensor mounted on the vehicle as the vehicle travels along a road segment, (b) receiving current terrain-based data corresponding to the road segment on which the vehicle is traveling from one or more other sensors on the vehicle, (c) orienting the vehicle in a travel lane of the road segment based on a comparison of the current terrain-based data from (b) to stored terrain-based data associated with one or more lanes of the road segment, (d) calculating a lateral offset or discrepancy between a location based on the GNSS signals and a location based on the terrain-based data, and (e) applying the lateral offset to the raw GNSS data to determine a corrected GNSS location of the vehicle for subsequent positions of the vehicle over a period of time.

[0006] In some implementations, the method also includes transmitting the corrected GNSS location of the vehicle to one or more controllers on the vehicle. In some examples, the one or more controllers on the vehicle include at least one of an ADAS controller, a semi-autonomous driving controller, or an autonomous driving controller.

[0007] In some implementations, the method also includes recalculating the lateral offset each time the vehicle travels a predetermined distance.

[0008] In some implementations, the method also includes recalculating the lateral offset at the end or start of each road segment.

[0009] In some implementations, the method also includes determining a GNSS error for a particular region based on a lateral offset between the raw GNSS signal and a GNSS location associated with the stored terrain-based data, and transmitting the GNSS error to other vehicles in the region.

[0010] According to one aspect, the present disclosure provides a method for determining a travel lane of a vehicle while the vehicle is traveling along a multi-lane road segment. The method includes collecting current road profile information using at least one on-board sensor, receiving from a database previously collected representative road profile information and representative lateral position data for each of at least two travel lanes associated with the road segment, comparing the current road profile information with the received representative road profile information for each of the at least two travel lanes, selecting a travel lane from among the at least two travel lanes associated with the road segment whose representative road profile information is most similar to the current road profile information, and determining the current travel lane as the selected travel lane. In some implementations of the method, the travel lane is an unmarked travel lane associated with the road segment. In some implementations of the method, the current road profile information is a current road surface profile and each of the previously collected representative road profile information is a previously collected representative road surface profile. In some implementations, the method also includes determining a lateral position of the current travel lane as a lateral position of the selected travel lane. In some implementations, the method also includes determining a first lateral position of the at least one point along the current driving lane based on information from the GNSS receiver when the vehicle is located at the at least one point along the current driving lane, determining a second lateral position of the at least one point along the current driving lane based on the lateral position of the current driving lane, and determining an error in the information received from the GNSS receiver based at least in part on a discrepancy between the first lateral position and the second lateral position. [Brief description of the drawings]

[0011] BRIEF DESCRIPTION OF THE DRAWINGS [Figure 1] 1 illustrates an exemplary two-lane road section. [Diagram 2]2 is an exemplary probability density function of a Global Navigation Satellite System (GNSS)-based lateral position coordinate of a vehicle traveling in the right lane of the road section shown in FIG. 1 . [Diagram 3] The cumulative distribution function of the basic distribution in Figure 2 is shown. [Figure 4] 2 is an exemplary probability density function for the GNSS-based lateral position coordinates of a vehicle traveling in the right and left lanes of the road section shown in FIG. 1 . [Diagram 5] 1 shows an exemplary distribution of lateral offset readings obtained from ten traverses of a road section in each lane. [Figure 6] 2 illustrates a grouping of lateral offset readings associated with each lane in FIG. 1; [Figure 7] 1. An exemplary method for determining which groupings in FIG. 6 belong to the left lane and which groupings belong to the right lane of the road segment in FIG. 1 is shown. [Figure 8] An example of GNSS augmentation performed on the basis of terrain-based information is shown. [Figure 9] 1 illustrates an example method for performing GNSS augmentation based on terrain-based information. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Detailed Description In some embodiments, data related to the road surface may be collected by using one or more sensors (e.g., accelerometers, position sensors, etc.) attached to one or more points on the vehicle (e.g., attached to the vehicle's wheels, the vehicle's wheel assemblies, dampers, the vehicle's unsprung mass, or a portion of the vehicle's sprung mass) as the vehicle travels along the road (or road section). This data may be used to map certain characteristics of the road surface, for example, by determining a road surface profile and / or the presence, location, and / or extent of various irregularities. It is noted that this disclosure describes using road surface characteristics (such as a road surface profile) in conjunction with GNSS to determine the number of lanes in a road section and / or their relative lateral positions. However, it is further noted that in embodiments disclosed herein, sub-surface characteristics of the road (e.g., as determined by ground penetrating radar) may be used in addition to or in place of the road surface characteristics. Using sub-surface characteristics in addition to or in place of the road surface characteristics is contemplated, as the disclosure is not so limited.

[0013] In some embodiments, the road surface characteristics of a road segment can be mapped by combining (e.g., averaging) data collected during multiple traverses of the road segment by one or more vehicles. For example, in some embodiments, road surface profile data from multiple traverses of the road segment can be averaged to generate a representative road surface profile for the road segment. Such a representative road surface profile can be stored remotely (e.g., in the cloud) or on-board the vehicle and then provided or made available to the vehicle. A vehicle receiving or accessing such data, for example, from the cloud or on-board data storage, can also collect current road surface information. By comparing current data collected by the vehicle during a current trip with previously stored road surface data (e.g., road surface profiles), the vehicle can determine its longitudinal position on the road surface.

[0014] However, on a multi-lane road, the road surface and / or sub-surface characteristics of the various lanes may lack similarity, such that average data (e.g., an average road surface profile based on data from multiple lanes) may not be representative of any one of the lanes of travel or the entire road segment. In addition, the road surface and / or sub-surface characteristics (e.g., road surface and / or sub-surface profile) of one lane of a multi-lane road may be significantly different from the road surface or sub-surface characteristics of a second lane, such that it may not be possible to localize a vehicle traveling along the second lane based on a comparison of previously stored data and current data, for example, received from the cloud. Thus, in some embodiments, it is desirable to know representative characteristics (e.g., road surface profile) of each of the multiple lanes on a multi-lane road.

[0015] The inventors recognize that data collected from multiple lanes of a multi-lane road during multiple traverses can be used to determine both the number of lanes and characteristics (e.g., road surface profile) of two or more of those lanes. In some embodiments, the number of lanes and the road surface characteristics of each of those lanes can be determined without relying on a priori knowledge regarding the number of lanes and / or the lanes of travel during the traverses during which the data was collected.

[0016] In some embodiments, road profile information may be acquired from multiple runs along a road segment by one or more vehicles across multiple lanes of the road segment and stored, e.g., in the cloud. In some embodiments, each road profile record in the dataset may correspond to a different traverse of a given road segment. For example, a series of road profiles may correspond to multiple traverses of a given road segment by one vehicle. Distinct road profiles may be measured and stored multiple times as one vehicle traverses the road segment. Alternatively, data may be collected by multiple (different) vehicles as they traverse a given road segment, and distinct road profiles may be measured at each of the multiple traverses. Such data may be collected by any suitably equipped and configured vehicle (e.g., equipped with hardware and software to acquire road profiles and transmit the acquired road profile information for storage remotely (e.g., on the cloud)) that traverses the road segment, resulting in a series of road profiles for the road segment.

[0017] In certain embodiments, once it is determined that the set of road surface characteristics (e.g., road profiles along a road segment) includes a sufficient number of samples, a correlation clustering algorithm can be applied to the data set. Correlation clustering algorithms that can be used can include, for example, hierarchical or partitional optimized clustering methods (e.g., k-means, c-means, principal component analysis, agglomerative hierarchical clustering, partitional clustering, Bayesian clustering, spectral clustering, etc.). Based on the results of the clustering procedure, the set of road characteristics (e.g., road surface profiles) can be divided into one or more clusters, where each of the road profiles included in a given cluster is substantially or sufficiently similar to each of the other road profiles included in the given cluster. In some embodiments, the algorithm can ignore certain road profiles that do not appear in a sufficiently large cluster. For example, some or all of a set of road profiles based on data collected during multiple traversals of a road section may be divided into at least a first cluster of road profiles and a second cluster of road profiles, where each of the road profiles in the first cluster is substantially or sufficiently similar to each of the other road profiles in the first cluster, and each of the road profiles in the second cluster is substantially or sufficiently similar to each of the road profiles in the second cluster.

[0018] In certain embodiments, each cluster may represent a single travel lane of a road or road segment. In certain embodiments, some or all of the average characteristics of the clusters (e.g., road surface profiles in a given cluster) may be averaged to obtain a single representative road surface characteristic for a particular lane. This lane average road profile may serve as a reference or representative road profile for a given lane in the road segment. Such reference or representative road profile information may be used, for example, for terrain-based localization or preview control of the vehicle (e.g., controlling one or more vehicle systems (e.g., active or semi-active suspension, steering and / or braking systems) based on subsequent knowledge of the road surface characteristics). Such information may be stored in a database and associated with a particular lane in the road segment. As used herein, the term "terrain-based localization" refers to a process of locating or determining the position of a vehicle based at least in part on a comparison of road profile information (e.g., road surface profile and / or sub-surface profile) collected by the vehicle during a current traversal of the road segment to previously stored road profile information (e.g., road surface profile and / or sub-surface profile) associated with the road segment.

[0019] This averaging may be performed for each of the identified clusters. In certain embodiments, the clustering algorithm may be repeated periodically (e.g., after a certain number of new road profiles have been collected for a given road segment). Alternatively, the clustering algorithm may be repeated each time new road profile data is collected to determine which cluster the most recent profile belongs to.

[0020] In some embodiments, clustering may utilize an agglomerative hierarchical method that initializes each road profile as its own cluster and then recursively merges the most similar cluster pairs until a stopping criterion is met. In some embodiments, the stopping criterion may consist of a small number of different Boolean values ​​related to the absolute similarity of the candidate cluster pairs, the relative similarity of the hypothetical merged cluster to the original cluster pairs, and the valid frequency range of the road profiles of each cluster. Road profiles may be completely excluded from the clustering process for various reasons. For example, if a lateral or lane-changing maneuver that deviates from the expected road curvature is detected, then the particular road profile may be excluded and not associated with a particular cluster or lane.

[0021] In some embodiments, rather than considering each cluster to represent the characteristics of a lane, only clusters with a number of road profiles above a certain threshold may be considered lanes. For example, a cluster with a single road profile or a small number of profiles below a threshold may be considered an outlier rather than a completely different lane. An outlier may occur, for example, when a vehicle changes lanes, exits and re-enters a lane, or leaves the road entirely. In certain embodiments, road profiles considered to be outliers may be deleted or ignored, for example, after a preset time, to conserve storage, to avoid confusion, or for other suitable reasons.

[0022] As used herein, the term "lane" refers to a path of travel for a vehicle traversing a road segment. A lane may or may not be physically marked by lane dividers or signs. For example, a road segment without lane markers may still have multiple lanes.

[0023] As used herein, the term "road segment" refers to a continuous portion of a road in a road network, of any suitable length, having a start point and an end point, which may be straight or curved, and which may include intersections with other roads or road segments.

[0024] The inventors recognize that clustering can be used to determine the number of travel lanes (marked or unmarked) for a road segment, as well as the associated representative or reference road surface profile (or subsurface profile) for each lane, however, this method may not indicate the relative lateral positioning or ordering of the identified lanes.

[0025] The inventors further recognize that as a vehicle traverses a road segment, one or more lateral position coordinates (e.g., relative to the road surface) may be associated with a particular path taken during the traverse. The lateral position may be determined, for example, by using data received from a GNSS (e.g., GPS) receiver on board the vehicle. In some embodiments, this lateral position information may also be associated with one or more road surface and / or sub-surface characteristics (e.g., road surface profile) measured during the traverse. The lateral position associated with the traverse may be determined, for example, at random points during the traverse or at a particular pre-selected longitudinal position (e.g., at the start, midpoint, or end of the road segment). Alternatively, the lateral position associated with the traverse and / or road surface characteristics may be determined by averaging multiple lateral position readings obtained during a given traverse.

[0026] In some embodiments, after the road surface characteristics (e.g., road profile, road surface profile, sub-road surface profile) are clustered, the lateral position of each cluster can be determined by averaging the lateral position readings associated with each member of the cluster. The average lateral position of each cluster can then be associated with the lateral position of the lane. The average lateral position of each lane thus determined can be used to further determine the lateral position of a given lane of a multi-lane road section. The inventors recognize that a combination of the clustering of road surface profiles and GNSS tracking of a vehicle traversing a road section can be used to infer, for example, the lateral position of the lane relative to the road section, the absolute lateral position of the lane, the number of lanes in a multi-lane road section and the ordering of those lanes. The inventors further recognize that the lane of travel of the vehicle can be determined by comparing the current road surface information (e.g., road surface profile and / or sub-road surface profile) with pre-recorded average, lane-specific, road surface and / or sub-surface characteristics of the multi-lane road section.

[0027] In some embodiments, lane ordering can be achieved by 1) collecting terrain-based data from multiple traverses of a road surface by one or more vehicles, 2) clustering the data to determine which drives are associated with each of two or more lanes, 3) averaging GNSS coordinates associated with each traverse in each lane, and 4) determining the ordering and / or positioning of the lanes based on the average GNSS coordinates of each lane.

[0028] In some embodiments, averaging GNSS traces from multiple drives can improve the lateral offset accuracy of GNSS readings by mitigating slowly varying lateral offsets of the GNSS readings. As used herein, the term "slowly varying lateral offset" refers to an inherent lateral GNSS position error that does not change significantly during traversal of a road section. In some embodiments, GNSS data from at least 5 and less than 10 traverses can be averaged to achieve a sufficient level of lateral offset accuracy and to effectively determine lane ordering. In some embodiments, data from at least 5 and less than 20 traverses can be averaged to achieve a sufficient level of lateral offset accuracy and to effectively determine lane ordering. In some embodiments, data from at least 5 and less than 1000 traverses can be averaged to achieve a sufficient level of lateral offset accuracy and to effectively determine lane ordering. However, both numbers of traverses above and below the above ranges are contemplated as the disclosure is not so limited.

[0029] In some embodiments, knowledge of the absolute or relative lateral position and / or ordering of lanes in a given road segment may be useful to more quickly determine the location of the vehicle. For example, in some embodiments, such information may be used to determine which lane the vehicle is traveling in after a recognized lane change before having to rely on pattern matching of road profiles. For example, if the vehicle is known or known with a sufficient degree of certainty to be traveling in lane X (e.g., the right lane of a three-lane road) and a subsequent lane change maneuver to the left is detected (e.g., based on signals from an IMU or other sensor capable of detecting yaw), this information may be used as an indication that the vehicle is in the lane to the left of lane X (e.g., the center lane). If such information is not available, the new location may be determined by using one or more localization techniques (e.g., terrain-based localization in "lane seek" mode). In such a mode, the road profile collected by the vehicle may be compared to all or some of the previously collected road surface profiles of known lanes of the road. Then, after a matching profile is found, the lane in which the vehicle is traveling may be determined. The lane seeking mode may be more time consuming and / or computationally intensive than, for example, determining or estimating the driving lane based on yaw sensor readings and knowledge of lane ordering. For example, in some embodiments, the vehicle's driving lane may be determined based on information from on-board sensors (e.g., an IMU, one or more accelerometers) and information about lane ordering, before or without relying on localization techniques (e.g., visually viewing the current road surface profile after a maneuver or matching the current road surface profile after a maneuver with stored representative road surface profiles of multiple lanes, etc.).

[0030] Figure 1 illustrates an exemplary road segment 10 including a right lane 12 and a left lane 14. In this example, the lanes are marked and each lane is 3.7 meters wide. However, marked and unmarked lanes wider and narrower than those shown in Figure 1 are also contemplated as the disclosure is not so limited. Note that in subsequent figures, the y-axis (or vertical axis) changes to represent different amounts, but the x-axis consistently indicates lateral offset.

[0031] In some embodiments, a vehicle traveling along the road section of FIG. 1 may include an on-board GNSS receiver. Errors in GNSS measurements when using such a receiver may have a standard deviation of approximately 5 meters or more. For example, an exemplary GNSS receiver on-board a vehicle traveling in lane 12 may exhibit the probability density function 20 shown in FIG. 2. As shown in FIG. 2, the majority of the distribution is located outside the right lane, e.g., to the left of the centerline of the road (i.e., in the example shown in FIG. 2, left lane 14). In some embodiments, the standard deviation of the vehicle's lateral position measured by the GNSS may be in the range of 5 to 10 meters. Standard deviations above the 5 to 10 meter range and below the 5 to 10 meter range are contemplated as the disclosure is not so limited.

[0032] Figure 3 shows the cumulative distribution function 30 of the underlying distribution of Figure 2. In Figure 3, approximately 35% of the area under the curve is located to the left of the centerline of the road segment 10. Thus, according to the cumulative distribution function 30, for each vehicle traveling in the right lane of the road segment 10, a GNSS-only system may indicate approximately 35% of the time that the vehicle is traveling in the left lane when in fact it is traveling in the right lane. A random guess (e.g., by flipping a coin) as to which lane a vehicle is traveling in on the road of Figure 1 would be correct 50% of the time.

[0033] Figure 4 shows a probability density function 20 for a vehicle actually traveling in the right lane and a probability density function 40 for a vehicle actually traveling in the left lane. In the example shown in Figure 4, the lateral GNSS coordinate of any vehicle traveling on the road section 10 can be determined by one of these distributions depending on which lane the vehicle is actually traveling in.

[0034] In Figure 5, the twenty open circles 50 represent twenty lateral offset readings at a particular longitudinal location of the road segment 10 associated with ten traverses in the right lane 12 and ten traverses in the left lane 14 of the road segment 10. In the example shown in Figure 5, it is not possible to effectively distinguish between readings associated with ten vehicles traveling in the left lane and ten vehicles traveling in the right lane based solely on the GNSS coordinate readings. This is because of the inherent accuracy of the GNSS readings shown in Figure 4.

[0035] However, clustering based on terrain-based similarity of road surface or sub-surface characteristics (e.g., road surface profile), the GNSS data collected during the 20 traverses of the road section of FIG. 5 can be grouped based on the lane of travel. Lateral offsets from drives in the same lane can be grouped together and associated with a particular lane, independent of the GNSS measurements. As discussed above, terrain-based clustering can also indicate the number of lanes in a road section without knowing the number of lanes a priori. However, the clustering analysis may not be sufficient to indicate the relative positioning of the lanes. After the clustering step, the relative positions of the lanes identified by the clustering may not be obvious.

[0036] In Figure 6, the open circles 60 represent readings associated with a first lane of a two-lane road, and the closed circles 62 represent readings associated with a second lane. In Figure 6, the circles 60, 62 are plotted according to the lateral offset that each circle represents. By comparing the average value of the closed and open circles, it can be determined that the first lane is located to the left of the second lane.

[0037] Figure 7 shows an example of a process for determining which clusters from Figure 6 belong to the left lane or which clusters belong to the right lane. Using the cluster designations from the terrain-based clustering, GNSS measurements that belong to the same cluster can be averaged to obtain an estimate of the centerline offset for each lane. The uncertainty or standard deviation of the lane center estimate can be, for example,

number

[0038] FIG. 8 shows a graph 800 (both axes in meters) illustrating an example of correcting or augmenting a current GNSS reading by relying on terrain-based information about the travel lanes of a road segment.

[0039] In some implementations, as the vehicle travels along a previously mapped road surface and is equipped with sensors attached to various sprung and unsprung components of the vehicle, such as, but not limited to, steering angle sensors, GNSS antennas and receivers, accelerometers and / or inertial measurement units, data received from the sensors may be recorded and / or filtered to remove unwanted noise. This data may be combined in various ways to infer the behavior of the vehicle on the road.

[0040] In some implementations, the recorded GNSS data may have an inherent low-frequency drift over time as the vehicle traverses the road section. In some implementations, the low-frequency drift may be 0.05 Hz or less, 0.1 Hz or less, or 1 Hz or less, as the disclosure is not limited to a particular drift rate. An example of such an inherent low-frequency drift is shown by line 802, depicting an exemplary raw GPS signal. This means that the GNSS signal may deviate slightly laterally and vertically from the actual position of the vehicle. The deviation may range from a few millimeters to a few meters, and the deviated GNSS trace may be slightly offset in the same lane as the vehicle traverses, or the GNSS trace may fall near or in an adjacent lane on either side of the vehicle's actual travel lane. In some cases, the GNSS trace may deviate up to 5 meters or more in one direction. If this deviation signal were used to position the vehicle in an absolute sense, the vehicle would be positioned far away from its actual location, which could result in erroneous road surface preview signals being sent to a controller (e.g., an autonomous driving controller, a semi-autonomous driving controller, an ADAS controller, etc.) that, for example, attempts to center the vehicle in the lane.

[0041] In some embodiments, accelerometer readings from one or more corners of the vehicle can be used to build a road surface profile (e.g., consisting of disturbances in a particular frequency range or ranges, e.g., in the direction of the vertical axis of each wheel). This signal can be thought of as a kind of fingerprint of the road surface that is unique within a particular geographic area, road, or road section. This fingerprint or road profile can also be used to distinguish lanes, since each travel lane on a multi-lane road may have characteristic irregularities (such irregularities can be incorporated, for example, in a referenced or stored road surface profile associated with the travel lane of the road section). These irregularities or road surface profiles can be recorded in real time as the vehicle travels along the road. These generated road profiles can then be compared to the stored road profiles of the various travel lanes to find a match.

[0042] In some embodiments, lateral position errors received from GNSS signals during a current traverse of a road segment may be corrected, at least in part, based on the use of a combination of previously collected road profile information (e.g., road surface profile and / or sub-surface profile) and associated previously collected GNSS data. In some embodiments, the similarity of the current terrain information (e.g., road surface profile and / or sub-surface profile) to previously collected terrain information for various travel lanes of a particular road segment may be used to determine the actual current travel lane of the vehicle on a multi-lane road segment.

[0043] In some embodiments, to make this determination, a buffer of a certain number of road profile data points may be collected. This buffering may be performed over the length of the road segment (which may be, for example, an 80 meter long road segment). However, this disclosure is not limited to a distance buffer size of a certain length. In some embodiments, at the time this information is collected, the vehicle's travel lane may be determined based on a similarity metric between the current data and reference data associated with a particular travel lane of the road segment.

[0044] In some embodiments, once the actual lane of travel has been determined, GNSS coordinates previously associated with the lane of travel, which may be determined as discussed above, may be retrieved from data storage, and these previously acquired and recorded GNSS coordinates may be used to at least partially correct for errors in the current GNSS information.

[0045] In some embodiments, performing the comparison may involve transforming both the current GNSS trace and the previously collected GNSS trace from a global coordinate frame to a local coordinate frame. This transformation may involve, for example, using previously established methods for transforming spherical coordinates into planar coordinates so that the Euclidean distance can be calculated between the two GNSS coordinate sets. In some embodiments, the offset calculation may be reduced to find the lateral distance between the closest points between the two traces once the coordinates are set in, for example, a reference planar frame (e.g., XY). This offset may generally be consistent in the readings recorded by the GNSS receiver mounted on the vehicle. This consistency means that the calculated offset is constant or virtually constant even while traveling a significant distance (e.g., 80 meters). Under certain conditions, the calculated offset may be constant or virtually constant even while traveling a longer distance (such as 150 meters, 200 meters, or 250 meters), although the disclosure is not so limited.

[0046] As discussed above, in some embodiments, the calculated offset may not change over the length of the road segment (e.g., 80 meters). In such cases, the calculated offset value may be subtracted from the live GNSS (e.g., GPS) values ​​received during the current traverse. This process may be used to reduce the instantaneous inherent error in the GNSS readings. Once the live road profile is matched with a section of road stored in a database (i.e., the live road profile is matched with a previously determined road profile associated with the travel lane), the vehicle's travel lane is known and the vehicle can be located longitudinally as long as the similarity between the stored road profile and the live, currently determined road profile remains high enough. If this matching continues beyond a certain distance (e.g., the length of the road segment, half the length of the road segment, every 50 meters, every 60 meters, every 70 meters, every 80 meters, etc.), a new lateral offset correction value may be calculated and applied to the live GNSS values, thus further reducing the accumulated lateral error. An example of a corrected GPS trace is shown by line 804 in FIG. 8, where a new lateral offset is repeatedly implemented to maintain the lateral error at approximately zero.

[0047] In some embodiments, the lateral error may eventually be small enough to meet targets or requirements for positioning the vehicle within a lane, which may be necessary to support applications such as lane-keeping assist ADAS features, semi-autonomous driving features, and / or autonomous driving features.

[0048] 9 illustrates an exemplary method for performing real-time GNSS augmentation or correction based on terrain-based information. The method includes (a) receiving GNSS signals corresponding to a location of the vehicle from a GNSS sensor on the vehicle as the vehicle travels along a road segment (902), (b) receiving terrain-based data corresponding to the road segment on which the vehicle is currently traveling from one or more other sensors on the vehicle (904), (c) orienting the vehicle in a driving lane of the road segment based on a comparison of the current terrain-based data from (b) to the stored terrain-based data (906), (d) calculating a lateral offset between the raw GNSS signals and a GNSS location associated with the stored terrain-based data (908), and (e) applying the lateral offset to the raw GNSS data to determine a corrected real-time GNSS location of the vehicle (910).

[0049] In some implementations, the method also includes transmitting the corrected GNSS location of the vehicle to one or more controllers on the vehicle. In some examples, the one or more controllers on the vehicle include at least one of an ADAS controller, a semi-autonomous driving controller, or an autonomous driving controller. These controllers may perform or assist with lane keeping assist functions, autonomous driving functions, and the like.

[0050] In some implementations, the method also includes recalculating the lateral offset each time the vehicle travels a predetermined distance or after a predetermined time has elapsed. The predetermined distance may be approximately the length of one road segment of the road data, which in some implementations may be approximately 80 meters. In some implementations, the method may also include recalculating the lateral offset at an end of each road segment or at a start of each road segment.

[0051] In some embodiments, information about the number of lanes in a road segment and / or lane-specific information about road surface characteristics can be provided to the vehicle from a remote data store (e.g., the cloud). Such information can be used within or by one or more microprocessors in the vehicle receiving the information to determine the location of the vehicle independently or in combination with the GNSS data and / or to control one or more systems of the vehicle (including, but not limited to, active or semi-active suspension systems, EPS, ABS, ADAS, ESC, HVAC and / or lighting systems).

[0052] Embodiments have been described in which the techniques are implemented in circuits and / or computer-executable instructions. It should be understood that some embodiments may be in the form of a method, of which at least one example is provided. The acts performed as part of a method may be ordered in any suitable manner. Accordingly, embodiments may be constructed in which acts are performed in an order other than that shown (which may include performing some acts simultaneously, even if shown as sequential acts in the illustrated embodiment).

[0053] The use of ordinal terms such as "first," "second," and "third" in the claims to modify claim elements does not, by itself, imply a priority, precedence, or order of one claim element over another, nor does it imply a chronological order in which acts of a method are performed, but is merely used as a label to distinguish one claim element having a certain name from another element having the same name (except for the use of ordinal terms).

[0054] Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of "including," "having," "containing," "involving," and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.

[0055] The word "exemplary" is used herein in the sense of serving as an example, instance, or illustration. Thus, unless otherwise indicated, any embodiments, implementations, processes, features, etc. described herein as exemplary should be understood as illustrative examples and not as preferred or advantageous examples.

[0056] Labeling of steps in any method claim, for example by use of labels such as "(a)", "(b)", "(c)", etc., is for convenience of reference and is not intended to indicate the particular order in which those steps occur. Having thus described several aspects of at least one embodiment, it should be understood that various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be part of this disclosure, and are intended to be within the spirit and scope of the principles described herein. Accordingly, the foregoing description and drawings are by way of example only.

Claims

Claim 1 A method for determining the lane order in a road section, comprising: (a) receiving a plurality of road surface profiles, each profile being based on data collected between each of a plurality of traverses of the length of the road section; (b) determining, between each traverse of (a), a representative lateral offset of each driving path; (c) determining the similarity between the road surface profiles of (a); (d) clustering the road surface profiles based on the similarity determined in (c), such that profiles having a similarity greater than a preset threshold are associated with the same lane; (e) determining an average value of the representative lateral offsets of (b) associated with each cluster of (d); (f) determining the lane order of the road section based on the average value of (e). A method comprising the above steps. Claim 2 The method according to claim 1, further comprising determining the distance between the calculated center lines of at least two lanes of the road section. Claim 3 A method for determining the lateral position of a driving lane in a road section, comprising: (a) traversing the road section along a plurality of driving paths using at least one vehicle; (b) determining the road surface profile and lateral position of each of the plurality of driving paths of (a); (c) clustering the road surface profiles determined in (b); (d) identifying a first cluster containing a sufficient number of road surface profiles, the sufficient number being greater than a preset threshold; (e) determining a representative road surface profile of the first cluster of (d); (f) determining a representative lateral position of a series of driving paths corresponding to the road surface profiles within the first cluster; (g) determining the lateral position of the driving lane based on the representative lateral position determined in (f). A method comprising the above steps. Claim 4 The method according to claim 3, wherein the representative lateral position of (f) is determined by averaging the lateral positions of the series of driving paths. Claim 5 The method according to claim 3, wherein the lateral position of the driving lane is equal to the representative lateral position determined in (f). Claim 6 The method according to any one of claims 3 to 5, wherein the representative lateral position of the series of drive paths in (f) is determined based on GNSS measurement values obtained while traveling along each of the series of drive paths in (f).

7. The method according to claim 6, wherein at least one GNSS measurement value is obtained while traveling along each drive path.

8. The method according to any one of claims 3 to 5, wherein the number of lanes is equal to the number of clusters containing a sufficient number of road surface profiles.

9. The method according to any one of claims 3 to 5, further comprising determining the driving lane of a vehicle traveling along the road section based on representative road surface profile data associated with the driving lane.

10. The method according to claim 9, wherein the representative road surface profile data is received from a cloud data storage system.

11. A method for determining the lane ordering in a road section, comprising: (a) receiving information about the characteristics of the road substructure below a plurality of drive paths, wherein the characteristics are based on data collected while driving over each of the plurality of drive paths in the road section; (b) determining a representative lateral offset for each of the plurality of drive paths in (a); (c) determining the similarity between the received information about the characteristics of the road substructure below the plurality of drive paths in (a); (d) clustering the information in (c), wherein clusters having a similarity greater than a preset threshold are associated with the same lane in the road section; (e) determining an average value of the representative lateral offsets in (b) associated with each of the drive paths within each cluster in (d); (f) determining the lane ordering in the road section based on the average value in (e). A method comprising the above steps.

12. A method for enhancing a GNSS signal corresponding to the location of a vehicle, comprising: (a) receiving, from a GNSS sensor on the vehicle, a raw GNSS signal corresponding to the location of the vehicle while the vehicle travels through a road section; (b) receiving, from one or more other sensors on the vehicle, terrain-based data corresponding to the road section on which the vehicle is traveling; (c) Positioning the vehicle in the lane of the road section based on a comparison between the terrain-based data from (b) and the stored terrain-based data; (d) Calculating a lateral offset between the raw GNSS signal and a GNSS location associated with the stored terrain-based data; (e) Applying the lateral offset to the raw GNSS data to determine a corrected GNSS location of the vehicle A method comprising. **Claim 13** The method according to claim 12, further comprising transmitting the corrected GNSS location of the vehicle to one or more controllers on the vehicle. **Claim 14** The method according to claim 13, wherein the one or more controllers on the vehicle include at least one of an ADAS controller, a semi-autonomous driving controller, or an autonomous driving controller. **Claim 15** The method according to claim 13, further comprising recalculating the lateral offset each time the vehicle travels a predetermined distance. **Claim 16** The method according to claim 13, further comprising recalculating the lateral offset at the end point or the start point of each road section. **Claim 17** The method according to claim 13, further comprising determining a GNSS error for a specific area based on the lateral offset between the raw GNSS signal and a GNSS location associated with the stored terrain-based data, and transmitting the GNSS error to other vehicles within the area. **Claim 18** A method for determining a driving lane of a vehicle while the vehicle is traveling along a multi-lane road section, comprising: Collecting current road profile information using at least one mounted sensor; Receiving, from a database, previously collected representative road profile information and representative lateral position data for each of at least two driving lanes associated with the road section; Comparing the current road profile information with the received representative road profile information for each of the at least two driving lanes; Selecting, from among the at least two driving lanes associated with the road section, the driving lane for which the representative road profile information is most similar to the current road profile information; Determining the current driving lane as the selected driving lane A method comprising.

19. The method according to claim 18, wherein the driving lane is an unmarked driving lane associated with the road section.

20. The method according to claim 18 or 19, wherein the current road profile information is a current road surface profile, and each of the previously collected representative road profile information is a previously collected representative road surface profile.

21. The method according to claim 18 or 19, wherein the current road profile information is a current subsurface profile, and each of the previously collected representative road profile information is a previously collected representative subsurface profile.

22. The method according to claim 18 or 19, further comprising determining the lateral position of the current driving lane as the lateral position of the selected driving lane.

23. When the vehicle is located at at least one point along the current driving lane, determining a first lateral position of the at least one point based on information from a GNSS receiver; determining a second lateral position of the at least one point along the current driving lane based on the lateral position of the current driving lane; determining an error in the information received from the GNSS receiver based at least in part on a discrepancy between the first lateral position and the second lateral position The method according to claim 22, further comprising.

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