Patent Publication Number: US-11654918-B2

Title: Estimation of road centerline based on vehicle telemetry

Description:
The subject disclosure relates to the estimation of a road centerline based on vehicle telemetry. 
     Semi-autonomous and autonomous vehicles (e.g., automobiles, trucks, farm equipment, construction equipment) use high-definition or medium-definition maps to navigate. The maps may be generated in a number of ways. For example, a vehicle equipped with sensors (e.g., camera, radar system, lidar system) may be driven over a roadway so that data may be recorded and road features (e.g., lane width and marking, road curvature) may be extracted from the data for inclusion in the map. As another example, a survey may be conducted using aerial images or lidar images. A roadway that conveys two-way traffic may have a centerline that is faded (e.g., due to high traffic) or occluded (e.g., by tree cover, snow, ice) or may have no centerline at all (e.g., a neighborhood road). In this case, the delineation between the two-way traffic must be determined and included in the map used for autonomous navigation to ensure that the vehicle is controlled to be on the correct side of the road. The previously noted sensor-based and survey-based mapping approaches can be resource-intensive. For example, multiple aerial surveys must be conducted of the same area in order to determine and delineate traffic flow. Accordingly, it is desirable to provide an estimation of a road centerline based on vehicle telemetry. 
     SUMMARY 
     In one exemplary embodiment, a method of estimating a centerline of a road that separates traffic moving in opposite directions includes aggregating a data set from each of a plurality of vehicles traversing the road over a period of time as telemetry data. Each data set of the telemetry data indicates a location and a heading. The method also includes clustering the data sets of the telemetry data based on the heading indicated by each data set, and identifying a separator to separate the data sets indicating a first heading from the data sets indicating a second heading, opposite to the first heading. The centerline is estimated based on applying a spatial smoothing to the separator. 
     In addition to one or more of the features described herein, the method also includes identifying a two-way segment of the road. 
     In addition to one or more of the features described herein, the aggregating and clustering the data set is from each of the plurality of vehicles traversing the two-way segment of the road. 
     In addition to one or more of the features described herein, the identifying the two-way segment of the road is based on a navigation map or on the telemetry data. 
     In addition to one or more of the features described herein, the method also includes identifying two or more two-way segments of the road based on a result of the clustering the data sets of the telemetry data. 
     In addition to one or more of the features described herein, the method also includes calculating a curvature of each portion of the road. 
     In addition to one or more of the features described herein, the identifying the separator includes using a logistic regression or a linear support vector machine (SVM) based on the curvature being less than a threshold value. 
     In addition to one or more of the features described herein, the identifying the separator includes using a non-linear SVM with a Gaussian or polynomial kernel based on the curvature being greater than a threshold value. 
     In addition to one or more of the features described herein, the method also includes indicating the centerline on a map. 
     In addition to one or more of the features described herein, autonomous operation of one or more vehicles includes using the map. 
     In another exemplary embodiment, a system to estimate a centerline of a road that separates traffic moving in opposite directions includes a memory device to store a data set provided by each of a plurality of vehicles traversing the road over a period of time. Each data set of the telemetry data indicates a location and a heading. The system also includes a processor to aggregate the data sets as telemetry data, to cluster the data sets of the telemetry data based on the heading indicated by each data set, to identify a separator to separate the data sets indicating a first heading from the data sets indicating a second heading, opposite to the first heading, and to estimate the centerline based on applying a spatial smoothing to the separator. 
     In addition to one or more of the features described herein, the processor identifies a two-way segment of the road. 
     In addition to one or more of the features described herein, the processor aggregates and clusters the data sets from each of the plurality of vehicles traversing the two-way segment of the road. 
     In addition to one or more of the features described herein, the processor identifies the two-way segment of the road based on a navigation map or on the telemetry data. 
     In addition to one or more of the features described herein, the processor identifies two or more two-way segments of the road based on a result of clustering the data sets of the telemetry data. 
     In addition to one or more of the features described herein, the processor calculates a curvature of each portion of the road. 
     In addition to one or more of the features described herein, the processor identifies the separator by using a logistic regression or a linear support vector machine (SVM) based on the curvature being less than a threshold value. 
     In addition to one or more of the features described herein, the processor identifies the separator by using a non-linear SVM with a Gaussian or polynomial kernel based on the curvature being greater than a threshold value. 
     In addition to one or more of the features described herein, the processor indicates the centerline on a map. 
     In addition to one or more of the features described herein, autonomous operation of one or more vehicles includes using the map. 
     The above features and advantages, and other features and advantages of the disclosure are readily apparent from the following detailed description when taken in connection with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Other features, advantages and details appear, by way of example only, in the following detailed description, the detailed description referring to the drawings in which: 
         FIG.  1    shows an exemplary system to estimate a road centerline based on vehicle telemetry according to one or more embodiments; 
         FIG.  2    is a process flow of a method of estimating a road centerline based on vehicle telemetry according to exemplary one or more embodiments; 
         FIG.  3    is a process flow of a method of estimating a road centerline based on vehicle telemetry according to other exemplary one or more embodiments; 
         FIG.  4    illustrates aggregating and clustering exemplary telemetry data according to one or more embodiments; and 
         FIG.  5    illustrates an exemplary center line of a road that is estimated based on telemetry data according to one or more embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The following description is merely exemplary in nature and is not intended to limit the present disclosure, its application or uses. It should be understood that throughout the drawings, corresponding reference numerals indicate like or corresponding parts and features. 
     As previously noted, a roadway that accommodates two-way traffic may not have a marked centerline that indicates the border between the traffic moving in each direction or may have a centerline that is faded. As a result, typical mapping approaches such as aerial surveying or sensor-based approaches cannot readily discern the centerline. Embodiments of the systems and methods detailed herein relate to an estimation of a road centerline based on vehicle telemetry. Telemetry data obtained by a number of vehicles is aggregated and clustered in order to find a separator of the clusters as an estimate of the centerline. 
     In accordance with an exemplary embodiment,  FIG.  1    shows an exemplary system to estimate a road centerline based on vehicle telemetry. Two exemplary vehicles  100  are shown in  FIG.  1   . One is an automobile  101  and the other is a truck  102 . The automobile  101  and the truck  102  are shown to include a global positioning system (GPS)  110  and other sensors  130  (e.g., lidar system, radar system, one or more cameras). They also include a processor  120 . The processor  120  of each vehicle  100  may obtain information from the GPS  110  and other sensors  130  that is used to augment or automate operation of the vehicle  100 . In addition, as shown, the processor  120  may facilitate communication with a controller  140 . Each processor  120  and the controller  140  may use processing circuitry that may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. 
     The controller  140  may perform cloud-based communication, as shown, or may perform cellular or other wireless communication with multiple vehicles  100  over a period of time. The controller  140  may be part of a vehicle  100  itself or may be included in infrastructure at a particular location. The vehicles  100  may provide telemetry data to the controller  140 . Telemetry data includes position information for the vehicle  100  based on the GPS  110 . Telemetry information also includes information indicating a direction and speed of the vehicle  100  as well as additional information such as elevation, for example. The controller  140  may store the telemetry data (i.e., each data set  405  ( FIG.  4   )) received from each vehicle  100  for processing. By using telemetry information from a number of vehicles  100  that traverse the same roadway, the controller  140  estimates a centerline  510  ( FIG.  5   ) as detailed with reference to  FIGS.  2  and  3   . 
       FIG.  2    is a process flow of a method  200  of estimating a road centerline  510  ( FIG.  5   ) based on vehicle telemetry according to exemplary one or more embodiments. At block  210 , the method includes identifying a road  205  of interest. At block  220 , aggregating telemetry data for the road  205  includes the controller  140  receiving and storing telemetry data (e.g., GPS position information, direction, speed) from a number of vehicles  100  that traverse the road  205  over a period of time. A given vehicle  100  may provide telemetry data for the road  205  at more than one location on the road  205 . That is, telemetry data may be obtained by the vehicle  100  and provided to the controller  140  periodically, for example. If the vehicle  100  is on the road  205  for multiple periods, the vehicle  100  may provide telemetry data collected at each of those periods. In addition, a vehicle  100  travelling the road  205  at different times may provide telemetry data for the road  205  during each of those times. 
     Clustering the telemetry data based on heading, at block  230 , refers to separating telemetry data that indicates a direction of travel on the road  205  from telemetry data that indicates an opposite direction of travel on the road  205 . As part of this processing, noisy data may be filtered out. For example, telemetry data indicating a heading perpendicular to the road (e.g., based on a vehicle  100  turning into a driveway or other road) may be eliminated during the clustering. In addition, elevation indicated by the telemetry data may be used to filter out noisy data pertaining to vehicles on an overpass above the road  205  or on an underpass below the road  205 . The telemetry data itself (i.e., the heading or elevation indicated), information from a navigation map, or a combination of the two may be used to implement this filtering out of telemetry data that does not pertain to travel along the road  205 . 
     At block  240 , separating the road  205  into segments  245   a  through  245   n  (generally referred to as  245 ) includes identifying one-way and two-way portions of the road  205 . The centerline  510  ( FIG.  5   ) is only relevant for segments  245  of the road  205  that facilitate traffic in opposite directions of travel. Thus, portions of the road  205  that may be restricted to one-way traffic may be excluded from further processing according to the method  200 . If all of the road  205  is two-way, segments  245  may be selected according to shape or length. For example, the road  205  may be separated into segments  245  that are all the same length. Alternately, a straight portion of the road  205  may be designated as a separate segment  245  from an adjacent curved portion of the road  205 . 
     At block  250 , identifying a separator  410  ( FIG.  4   ) for the telemetry clusters refers to determining a separator  410  for each two-way segment  245  from block  240 . The process of obtaining the separator  410  is further discussed with reference to  FIG.  5   . For each segment  245 , the telemetry data that is clustered according to heading, at block  230 , is used to determine the separator  410 . Once the separator  410  for each two-way segment  245  is determined, applying spatial smoothing, at block  260 , includes putting the segments  245  together to provide a centerline  510  ( FIG.  5   ) for the road  205 . The processes of obtaining the separator  410  and then the centerline  510  are further discussed with reference to  FIG.  5   . The estimate of the centerline  510  may be included in the map used by the autonomous vehicle  100 . 
       FIG.  3    is a process flow of a method  300  of estimating a road centerline  510  ( FIG.  5   ) based on vehicle telemetry according to other exemplary one or more embodiments. According to the embodiment shown in  FIG.  3   , the method  300  includes obtaining a two-way road segment  245  of interest, at block  310 . Thus, separation of a road  205  into segments  245  is done prior to any processing of telemetry data rather than based on clustering of telemetry data (at block  230 ,  FIG.  2   ) as in the previously discussed exemplary embodiment. The identification of the two-way road segment  245  may use additional information such as a navigation map. Alternately, the telemetry data collected for the road  205  may be used to identify the two-way road segment  245  of interest, at block  310 , prior to any clustering. 
     At block  320 , aggregating telemetry data for the segment  245  includes limiting the telemetry data that is used to data with a location matching locations of the segment  245 . Exemplary aggregated telemetry data  325 , output from block  320 , is shown in  FIG.  4   . At block  330 , clustering telemetry data based on heading refers to separating telemetry data indicating a direction of travel from telemetry data indicating an opposite direction of travel, as in block  230  ( FIG.  2   ). As discussed with reference to  FIG.  2    and, specifically, the processing at block  230 , telemetry data may additionally be filtered to leave out data pertaining to a heading that is perpendicular to the segment  245  or travel on a different roadway above or below the segment  245 . Unlike the process at block  230 , the clustering, at block  330 , is limited to telemetry data associated with the segment  245  of interest which is known to be a two-way segment  245 . Exemplary clustered telemetry data  335 , output from block  330 , is shown in  FIG.  3   . 
     At block  340 , like at block  250  ( FIG.  2   ), identifying a separator  410  ( FIG.  4   ) for the telemetry clusters refers to determining a delineation between clusters associated with one heading and clusters associated with an opposite heading. This is further discussed with reference to  FIG.  5   . Unlike at block  250 , the identification of a separator  410  is for one two-way segment  245  at block  340 . Once the separator  410  for the segment  245  selected at block  310  is determined, applying spatial smoothing, at block  350 , may include putting the segment  245  together with other segments  245  of a road  205  or simply smoothing the separator  410  for the segment  245  of interest to provide a centerline  510  ( FIG.  5   ). 
       FIG.  4    illustrates aggregating and clustering exemplary telemetry data according to one or more embodiments. One exemplary two-way road segment  245  is shown with aggregated telemetry data  325 . Each data set  405  indicates a location (e.g., GPS position) along the segment  245  and other information such as direction, speed, and elevation, for example. According to the separation based on heading information provided for each data set  405 , clustered telemetry data  335  may be obtained for the exemplary segment  245  at block  330  ( FIG.  3   ). The clustered telemetry data  335  may be part of the information obtained for a road  205  at block  230  ( FIG.  2   ). As shown in  FIG.  4   , the data sets  405  are color coded to distinguish those associated with a heading in one direction from those associated with a heading in the opposite direction. The separator  410  shown in  FIG.  4    is a demarcation of the clustered telemetry data  335  according to heading. Thus, most or all of the data sets  405  associated with one heading are separated by the separator  410  from most or all of the data sets  405  associated with an opposite heading. The process of determining the separator  410  is further discussed with reference to  FIG.  5   . 
       FIG.  5    illustrates an exemplary center line  510  of a road  205  that is estimated based on telemetry data according to one or more embodiments. Specifically, the centerline  510  is estimated based on the separator  410 . Estimating the separator  410  results from the processing at block  250  ( FIG.  2   ) or block  340  ( FIG.  3   ) according to exemplary embodiments. As previously noted, the separator  410  of a segment  245  of the road  205  may be identified for multiple segments  245 , at block  250  ( FIG.  2   ), or for one segment  245  at a time, at block  340  ( FIG.  3   ). While a segment  245  alone may represent the road  205  of interest according to an exemplary embodiment, multiple segments  245  may be put together to estimate the centerline  510  of a road  205  instead. Thus, the process of applying spatial smoothing, at block  260  ( FIG.  2   ) or at block  350  ( FIG.  3   ), to output the estimate of the centerline  510  may include putting together multiple segments  245  and corresponding separators  410 . If the method  200  according to the embodiment shown in  FIG.  2    is used, then the position and connection of the segments  245  is known since separating the road  205  into segments  245  is part of the processing, at block  240 . Alternately, a navigation map may be used to determine how the segments  245  connect with one another to form the road  205 . 
     As  FIG.  5    shows, a road  205  may include curved areas  505   a  and straight areas  505   b  (generally referred to as area  505 ). A given segment  245  may include both curved areas  505   a  and straight areas  505   b . In the exemplary case shown in  FIG.  5   , the curved area  505   a  may be a segment  245  and the straight area  505   b  may be a different segment  245  of the road  205 . Estimating the centerline  510  from the one or more separators  410  of one or more segments  245  of the road  205  includes applying spatial smoothing to the connected separators  410 . Estimating each of the separators  410  includes processing straight areas  505   b  differently from curved areas  505   a . Thus, calculating curvature to determine which approach to take is part of identifying the separator  410 , at block  250  or block  340 . A triangle with three sides of lengths p 1 , p 2 , and p 3  is shown to fit the curved area  505   a  of the exemplary road  205  in  FIG.  5   . The curvature is given by: 
     
       
         
           
             
               
                 
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     In EQ. 1, area refers to the area of the triangle with the sides of lengths p 1 , p 2 , and p 3 . If the curvature of a given area  505  exceeds a threshold value, then the area  505  is deemed to be a curved area  505   a . Below the threshold curvature value, the area  505  is deemed to be a straight area  505   b . In straight areas  505   b , a logistic regression or a linear support vector machine (SVM) may be used to separate clustered telemetry data  335  and thereby obtain separator  410 . In curved areas  505   a , a non-linear SVM with a Gaussian or polynomial kernel may be used separate the clusters and obtain the separator  410 . 
     While the above disclosure has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from its scope. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the disclosure without departing from the essential scope thereof. Therefore, it is intended that the present disclosure not be limited to the particular embodiments disclosed, but will include all embodiments falling within the scope thereof