Patent Publication Number: US-2023162602-A1

Title: Communication system for determining vehicle context and intent based on cooperative infrastructure perception messages

Description:
INTRODUCTION 
     The present disclosure relates to a communication system and method for determining a vehicle’s context and intent based on cooperative infrastructure perception messages. 
     Cooperative sensor sharing involves wirelessly transmitting data collected by various sensors to neighboring host users or vehicles. Thus, a host vehicle may receive information about a sensed object from multiple neighboring users. In cooperative sensor sharing, remote vehicles and roadway infrastructure share data related to sensed objects with a host vehicle. For example, an infrastructure camera such as a red light or speed camera may capture data related to a remote vehicle, which is then transmitted to a host vehicle. 
     Vehicle-to-everything (V2X) is an all-encompassing term for a vehicle’s connected communications and includes both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2X) applications that involve broadcasting messages from one entity to a host vehicle. However, if a particular vehicle is not equipped with V2X technology, then the host vehicle only receives data related to the particular vehicle’s position, speed, location geometry, and heading based on cooperative sensor sharing from source such as, for example, the infrastructure camera. That is, in other words, the host vehicle does not receive information related to the particular vehicle’s context, which refers to a short history of the vehicle’s path, and intent, which refers to a short prediction of the vehicle’s intended path. 
     Thus, while current vehicle connected communications achieve their intended purpose, there is a need in the art for an approach that determines a vehicle’s context and intent when none is available. 
     SUMMARY 
     According to several aspects, a communication system that determines a context and an intent of a specific remote vehicle located in a surrounding environment of a host vehicle is disclosed. The communication system includes one or more controllers for receiving sensed perception data related to the specific remote vehicle. The one or more controllers execute instructions to determine a plurality of vehicle parameters related to the specific remote vehicle based on the sensed perception data. The one or more controllers associate the specific remote vehicle with a specific lane of travel of a roadway based on map data, where the map data indicates information related to lanes of travel of the roadway that the specific remote vehicle is traveling along. The one or more controllers determine possible maneuvers, possible egress lanes, and a speed limit for the specific remote vehicle for the specific lane of travel based on the map data. Finally, the one or more controllers determines the context and the intent of the specific remote vehicle based on the plurality of vehicle parameters, the possible maneuvers, the possible egress lanes for the specific remote vehicle, and the speed limit related to the specific remote vehicle. 
     In one aspect, a plurality of coordinate pairs based on a world coordinate system are converted into image frame coordinates for noise modeling based on a homography matrix, where the coordinate pairs represent a monitored area of the surrounding environment of the host vehicle. 
     In another aspect, the one or more controllers execute instructions to determine, by a Kalman filter, a plurality of error resilient vehicle parameters related to the specific remote vehicle based on a noise associated with converting noise associated with converting the coordinate pairs based on the world coordinate system into image frame coordinates. 
     In yet another aspect, the the one or more controllers execute instructions to divide an image representing the monitored area of the surrounding environment into a plurality of pixel bins. 
     In an aspect, the one or more controllers determine how many of the coordinate pairs based on the world coordinate system map to each pixel bin of the image and determine a distance covariance map and a velocity covariance map based for each pixel bin that is part of the image. 
     In another aspect, the the one or more controllers execute instructions to render image data that is a representation of the specific remote vehicle, and execute an object detection algorithm to detect the specific remote vehicle within the image data, where the specific remote vehicle that is detected is a detected object pixel. The one or more controllers match the detected object pixel with the velocity covariance map and the distance covariance map. 
     In yet another aspect, the the one or more controllers execute instructions to determine a noise associated with a bounding box based on a plurality of stationary images of the specific remote vehicle, and determine the pixel bins that are impacted by the noise associated with the bounding box. The one or more controllers calculate an average velocity covariance matrix and an average distance covariance matrix for each impacted pixel bin, and match pixels belonging to the detected object with the velocity covariance map and the distance covariance map. Finally, the one or more controllers send the world coordinates of the detected object and a matching velocity covariance and a matching distance covariance to a Kalman Filter based state tracking module. 
     In still another aspect, the one or more controllers execute instructions to determine when the specific remote vehicle is in a pocket lane. In response to determining the specific remote vehicle being in the pocket lane, the one or more controllers set the context as equal to a distance the specific remote vehicle traveled in the pocket lane plus a distance traveled in the adjacent lane. In response to determining the specific remote vehicle is not in the pocket lane, the one or more controllers set the context as equal to a length of a current lane of travel. 
     In an aspect, the the one or more controllers execute instructions to determine a type of travel allowed by a current lane of travel for the specific remote vehicle, where the type of travel includes through movement only and turns allowed. In response to determining the type of travel allowed by the current lane of travel is through movement only, the one or more controllers set the intent as a connecting egress lane having a length expressed as an intent distance. 
     In another aspect, the the one or more controllers execute instructions to determine a type of travel allowed by a current lane of travel for the specific remote vehicle, where the type of travel includes through movement only and turns allowed. In response to determining the current lane of travel for the specific remote vehicle allows for turns, the one or more controllers sets multiple values for the intent, where each value corresponds to a length a potential connecting egress lane. 
     In yet another aspect, the the one or more controllers execute instructions to determine a confidence level indicating a probability that the intent is accurate. 
     In still another aspect, the plurality of vehicle parameters indicate a position, speed, location geometry, and heading of the specific remote vehicle. 
     In one aspect, a method for determining a context and an intent of a specific remote vehicle located in a surrounding environment of a host vehicle is disclosed. The method includes receiving, by one or more controllers, sensed perception data related to the specific remote vehicle. The method includes determining, by the one or more controllers, a plurality of vehicle parameters related to the specific remote vehicle based on the cooperative infrastructure sensing message. The method also includes associating the specific remote vehicle with a specific lane of travel of a roadway based on map data, where the map data indicates information related to lanes of travel of the roadway that the specific remote vehicle is traveling along. The method further includes determining possible maneuvers, possible egress lanes, and a speed limit for the specific remote vehicle for the specific lane of travel based on the map data. Finally, the method includes determining the context and the intent of the specific remote vehicle based on the plurality of vehicle parameters, the possible maneuvers, the possible egress lanes for the specific remote vehicle, and the speed limit related to the specific remote vehicle. 
     In another aspect, the method includes converting a plurality of coordinate pairs based on a world coordinate system into image frame coordinates for noise modeling based on a homography matrix, where the coordinate pairs represent a monitored area of the surrounding environment of the host vehicle. 
     In yet another aspect, the method includes determining, by a Kalman filter, a plurality of error resilient vehicle parameters related to the specific remote vehicle based on a noise associated with converting noise associated with converting the coordinate pairs based on the world coordinate system into image frame coordinates. 
     In still another aspect, the method includes dividing an image representing the monitored area of the surrounding environment into a plurality of pixel bins, determining how many of the coordinate pairs based on the world coordinate system map to each pixel bin of the image, and determining a distance covariance map and a velocity covariance map based for each pixel bin that is part of the image. 
     In an aspect, the method includes rendering image data that is a representation of the specific remote vehicle, executing an object detection algorithm to detect the specific remote vehicle within the image data, where the specific remote vehicle that is detected is a detected object pixel, and matching the detected object pixel with the velocity covariance map and the distance covariance map. 
     In another aspect, the method includes determining a noise associated with a bounding box based on a plurality of stationary images of the specific remote vehicle, determining the pixel bins that are impacted by the noise associated with the bounding box, calculating an average velocity covariance matrix and an average distance covariance matrix for each impacted pixel bin, matching pixels belonging to the detected object with the velocity covariance map and the distance covariance map, and sending the world coordinates of the detected object and a matching velocity covariance and a matching distance covariance to a Kalman Filter based state tracking module. 
     In yet another aspect, the method includes determining when the specific remote vehicle is in a pocket lane. In response to determining the specific remote vehicle being in the pocket lane, the method includes setting the context as equal to a distance the specific remote vehicle traveled in the pocket lane plus a distance traveled in the adjacent lane. In response to determining the specific remote vehicle is not in the pocket lane, the method includes setting the context as equal to a length of a current lane of travel. 
     In another aspect, the method includes determining a type of travel allowed by a current lane of travel for the specific remote vehicle, where the type of travel includes through movement only and turns allowed. In response to determining the type of travel allowed by the current lane of travel is through movement only, the method includes setting the intent as a connecting egress lane having a length expressed as an intent distance. 
     In yet another aspect, the method includes determining a type of travel allowed by a current lane of travel for the specific remote vehicle, where the type of travel includes through movement only and turns allowed. In response to determining the current lane of travel for the specific remote vehicle allows for turns, the method includes setting multiple values for the intent, where each value corresponds to a length a potential connecting egress lane. 
     Further areas of applicability will become apparent from the description provided herein. It should be understood that the description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way. 
         FIG.  1    is a schematic diagram of a host vehicle including a communication system for determining a context and intent of a specific remote vehicle, according to an exemplary embodiment; 
         FIG.  2    is a diagram illustrating an exemplary environment where the host vehicle shown in  FIG.  1    receives cooperative infrastructure sensing messages related to a specific remote vehicle, according to an exemplary embodiment; 
         FIG.  3    is a block diagram of a controller that is part of the communication system shown in  FIG.  1   , according to an exemplary embodiment; 
         FIG.  4    is an exemplary illustration of image data rendering a representation of the specific remote vehicle that is part of the surrounding environment shown in  FIG.  2   , according to an exemplary embodiment; 
         FIG.  5 A  is a process flow diagram illustrating a method for determining a context of the specific remote vehicle, according to an exemplary embodiment; 
         FIG.  5 B  is a process flow diagram illustrating a method for determining an intent of the specific remote vehicle, according to an exemplary embodiment; and 
         FIG.  6    is a process flow diagram illustrating a method for determining the context and the intent for the specific remote vehicle, according to an exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses. 
     Referring to  FIG.  1   , an exemplary host vehicle  10  is illustrated. The vehicle  10  is part of a communication system  12  including a controller  20  in electronic communication with a plurality of sensors  22  and one or more antennas  24 . In the example as shown in  FIG.  1   , the plurality of sensors  22  include one or more radar sensors  30 , one or more cameras  32 , an inertial measurement unit (IMU)  34 , a global positioning system (GPS)  36 , and LiDAR  38 , however, it is to be appreciated that additional sensors may be used as well. The communication system  12  also includes one or more remote objects  40  located in a surrounding environment  26  of the host vehicle  10 , which is shown in  FIG.  2   . Referring to both  FIGS.  1  and  2   , in one embodiment the remote objects  40  include, but are not limited to, one or more remote vehicles  42  and remote infrastructure  44  that is based on a cooperative perception and communication system. For example, in the embodiment as shown in  FIG.  2   , the remote infrastructure  44  includes infrastructure cameras such as red light cameras as well as processor and communication modules (not shown). The controller  20  of the communication system  12  receives cooperative infrastructure sensing messages  46  related to a specific remote vehicle  42  based on vehicle-to-infrastructure (V2X). However, it is to be appreciated that the controller  20  of the communication system  12  may also receive cooperative infrastructure sensing messages  46  based on cellular signals instead. In an embodiment, the cooperative infrastructure sensing messages  46  related to the specific remote vehicle  42  may be determined by another vehicle (not shown) within the surrounding environment  26  instead of the remote infrastructure  44 . 
     In embodiments, the specific remote vehicle  42  does not include vehicle-to-vehicle (V2V) communication capabilities. Thus, the cooperative infrastructure sensing messages  46  sent to the controller  20  of the vehicle  10  only indicates information related to a position and dynamics of the specific remote vehicle  42 , and not a context and an intent of the specific remote vehicle  42 . The context of the specific remote vehicle  42  indicates a travel history, and the intent predicts an intended path of the specific remote vehicle  42 . As explained below, the disclosed communication system  12  determines the context and the intent of the specific remote vehicle  42  based on the position and dynamics indicated by the cooperative infrastructure sensing messages  46 . 
       FIG.  3    is a block diagram of the communication system  12 . In the embodiment as shown in  FIG.  2   , the controller  20  includes a tracking and detection module  50 , a coordinate transform module  52 , a raw position module  54 , a noise modeling module  56 , an object detection module  57 , a localization and map matching module  58 , a context module  60 , and a confidence and intent module  62 , however, it is to be appreciated that the communication system  12  may be a distributed computing system that determines the context and intent of the specific remote vehicle  42  upon one or more controllers of the remote infrastructure  44  shown in  FIG.  2   . The tracking and detection module  50  of the controller  20  receives the cooperative infrastructure sensing messages  46  related to the specific remote vehicle  42  ( FIG.  2   ), which include sensed perception data from a perception device such as an infrastructure camera. The tracking and detection module  50  determines a plurality of vehicle parameters  68  related to the specific remote vehicle  42  based on the cooperative infrastructure sensing messages  46 . In an embodiment, the plurality of vehicle parameters  68  indicate a position, detection time, dimensions, identifier, speed, location geometry, and heading of the specific remote vehicle  42  in addition to image data  64  collected by the remote infrastructure  44  (i.e., the red light cameras seen in  FIG.  2   ), however, it is to be appreciated that other information may be included as well. In one embodiment, the image data  64  is collected by a single camera, and therefore depth perception is limited. However, it is to be appreciated that the image data  64  may be collected from multiple cameras as well. The plurality of vehicle parameters  68  are then sent to the coordinate transform module  52 . The coordinate transform module  52  converts the position, which is expressed in camera or image frame coordinates, into global coordinates. The position, speed, location geometry, and heading is then sent to the raw position module  54 . The raw position module  54  determines location information as well as speed, acceleration, and heading parameters of the specific remote vehicle  42   
     As explained below, the noise modeling module  56  determines noise associated with converting coordinates from the world coordinate system (also referred to as the GPS coordinate system) into image frame coordinates. The noise modeling module  56  receives the plurality of parameters  68  and detected pixel coordinates x, y related to the specific remote vehicle  42  and a plurality of world coordinate pairs X, Y representing a monitored area of the surrounding environment  26  ( FIG.  2   ). Specifically, the world coordinate pairs X, Y indicate a latitude and a longitude of specific points located along a patch of roadway and are based on the world coordinate system. The noise modeling module  56  converts the plurality of coordinate pairs X, Y expressed based on the world (e.g., GPS) coordinate system into image frame coordinates based on a homography matrix. The homography matrix is a mapping between two planes, namely an image plane and a world coordinate plane (i.e., GPS coordinates). In an embodiment, the homography matrix is pre-computed and is stored in a memory of the controller  20 . 
     Once the image frame coordinates have been determined, the noise modeling module  56  then performs homography noise modeling by determining noise associated with converting the world coordinate pairs X, Y into image frame coordinates. Specifically, the noise modeling module  56  then divides an image representing the monitored area of the surrounding environment  26  ( FIG.  2   ) into a plurality of pixel bins. For example, the image may be divided into M × N pixel bins (such as 2 × 2 or 4 × 4). The noise modeling module  56  then determines how many of the world coordinates map to each pixel bin of the image. The noise modeling module  56  then determines a distance covariance map and a velocity covariance map for each pixel bin that is part of the image. The noise modeling module  56  determines the distance covariance map for each pixel bin of the image using distances between the world coordinates mapped to each pixel bin. The noise modeling module  56  determines the velocity covariance map for each pixel bin by using distances between the world coordinates that map to a specific pixel bin, divided by an inter-frame time. 
       FIG.  4    is an exemplary illustration of the image data  64  that is transmitted by the cooperative infrastructure sensing messages  46 . The image data  64  renders a representation of the specific remote vehicle  42  that is part of the surrounding environment  26  ( FIG.  2   ) of the host vehicle  10 . Referring to  FIGS.  3  and  4   , the object detection module  57  executes an object detection algorithm to monitor the image data  64  that is part of the plurality of parameters  68  to detect the specific remote vehicle  42 . For example, in one non-limiting embodiment, the object detection module  57  may execute a You Only Look Once (YOLO) algorithm to detect the specific remote vehicle  42  that is part of the image data  64 .  FIG.  4    illustrates the specific remote vehicle  42  as detected by the object detection module  57  as a detected object pixel  70 . The noise modeling module  56  then matches the detected object pixel  70  with the velocity covariance map and the distance covariance map. 
     Continuing to refer to  FIGS.  3  and  4   , the noise modeling module  56  receives a plurality of stationary images of the specific remote vehicle  42  as input, and determines a noise associated with a bounding box  72 . The bounding box  72  is a rectangle that bounds a detected object, which is the specific remote vehicle  42  located within the stationary images. The noise associated with the bounding box  72  is determined based on any available method such as, for example, an object detection algorithm. In embodiments, the noise associated with the bounding box  72  is expressed as a covariance matrix. The noise modeling module  56  then determines the pixel bins that are impacted by the noise associated with the bounding box  72 . The noise modeling module  56  then calculates an average velocity covariance matrix and an average distance covariance matrix for each impacted pixel bin. When an object is detected, the noise modeling module  56  matches pixels belonging to the detected object with the velocity covariance map and the distance covariance map, and the world coordinates of the detected object and a matching velocity covariance map and a matching distance covariance map are sent to a Kalman Filter based state tracking module. The Kalman Filter based state tracking module then determines the noise associated with converting the world coordinate pairs X, Y into image frame coordinates. A Kalman filter then determines a plurality of error resilient vehicle parameters  78  related to the specific remote vehicle  42  based on the noise associated with converting the world coordinate pairs X, Y into image frame coordinates. In an embodiment, the plurality of error resilient vehicle parameters  78  indicate a position, speed, and heading of the specific remote vehicle  42 . 
     Turning back to  FIG.  3   , the localization and map matching module  58  receives the plurality of error resilient vehicle parameters  78  related to the specific remote vehicle  42  as well as map data  80  from a road geometry database  82 , and determines possible maneuvers, possible egress lanes for the specific remote vehicle  42 , and a speed limit based on the input. Specifically, the map data  80  most relevant to a direction of travel for the specific remote vehicle  42  may be selected. The most relevant map data is based on a direction of travel of the specific remote vehicle  42 . For example, if the specific remote vehicle  42  is traveling from the North to the South, then the map data  80  related to road geometry running from the North to the South may be used. The map data  80  indicates information related to the lanes of travel of a roadway that the specific remote vehicle  42  is traveling along. For example, the map data  80  may indicate a number of lanes and types of lanes related to the roadway the specific remote vehicle  42  is travelling along. For example, the map data  80  may indicate the roadway includes three lanes, where the type of lanes include a left lane, a center lane, and a right lane. 
     The map data  80  further indicates attributes for each lane included in a roadway. The attributes indicate allowed maneuvers as well as connecting lanes. Maneuvers may refer to an allowed direction of travel such as allowed turns, through-only lanes, possible connecting lanes, starting point for a turn pocket lane, and speed limit. In the present example, the left lane may be a left turn only lane, the center lane is a through lane, and the right lane is a right turn only lane. The connecting lanes refer to the lanes that a vehicle may travel along after making a maneuver. The localization and map matching module  58  associates the specific remote vehicle  42  with a specific lane of travel of the roadway based on the map data  80 . The localization and map matching module  58  then determines the possible maneuvers, the possible egress lanes for the specific remote vehicle  42 , and the speed limit for the specific remote vehicle  42  for the specific lane of travel based on the map data  80 . 
     The localization and map matching module  58  sends the plurality of error resilient vehicle parameters  78 , the possible maneuvers, the possible egress lanes for the specific remote vehicle  42 , and the speed limit related to the specific remote vehicle  42  to the context module  60 . The context module  60  then determines the context  84  of the specific remote vehicle  42  based on the plurality of error resilient vehicle parameters  78 , the possible maneuvers, the possible egress lanes for the specific remote vehicle  42 , and the speed limit related to the specific remote vehicle  42 . The context  84  represents the travel history of the specific remote vehicle  62 , and in an embodiment is expressed as a travel history distance. 
       FIG.  5 A  is a process flow diagram illustrating a method  200  for determining the travel history distance, or context  84 , of the specific remote vehicle  42 . Referring specifically to  FIGS.  3  and  5   , the method  200  may begin at decision block  202 . In decision block  202 , the context module  60  checks a memory of the controller  20  to determine if the specific remote vehicle  42  is detected for the first time, and its associated information is saved in memory. If the answer is yes, then the method  200  proceeds to block  204 , and a previous travel history distance is used as the context  84  of the specific remote vehicle  42 . The method  200  may then terminate. However, if the answer is no, then the method  200  may proceed to decision block  206 . 
     In decision block  206 , the context module  60  determines if the specific remote vehicle  42  is in a pocket lane. If the specific remote vehicle  42  is in a pocket lane, then the method  200  may proceed to block  208 . In block  208 , in response to determining the specific remote vehicle  42  is in the pocket lane, assume the specific remote vehicle  42  made the change into the pocket lane from an adjacent lane. The method  200  may then proceed to block  210 . In block  210 , the context module  60  determines the context  84  is equal to a distance the specific remote vehicle  42  traveled in the pocket lane plus a distance traveled in the adjacent lane. The method  200  may then terminate. 
     In the event the context module  60  determines the specific remote vehicle  42  was is not in a pocket lane, the method  200  may then proceed to block  212 . In block  212 , the context module  60  determines the specific remote vehicle  42  was in a current lane of travel. The method  200  may then proceed to block  214 . In block  214 , the context module  60  determines the context  84  is equal to a length of the current lane of travel. The method  200  may then terminate. Therefore, the context module  60  determines when the specific remote vehicle  42  is in a pocket lane, and in response to determining the specific remote vehicle  42  being in the pocket lane, set the context  84  as equal to the distance the specific remote vehicle  42  traveled in the pocket lane plus the distance traveled in the adjacent lane. However, in response to determining the specific remote vehicle  42  not being in the pocket lane, the context module  60  sets the context  84  as equal to the length of the current lane of travel. It is to be appreciated that the context  84  may be limited to a predetermined threshold that may be determined based on the context module  60 . 
     Referring back to  FIG.  3   , the confidence and intent module  62  receives the plurality of error resilient vehicle parameters  78 , the possible maneuvers, the possible egress lanes for the specific remote vehicle  42 , and the speed limit related to the specific remote vehicle  42  from the localization and map matching module  58 , and determines a confidence level  86  and the intent  88  of the specific remote vehicle  42 . The confidence level  86  indicates a probability that the intent  88  calculated by the confidence and intent module  62  is accurate. In an embodiment, the confidence level  86  is measured in terms of percentages, which may be mapped to high, medium, and low levels. As seen in  FIG.  3   , the confidence and intent module  62  receives cached location information  92  from a vehicle database  90 , where the cached location information saves data related to previous calculations for the confidence level. 
       FIG.  5 B  is a process flow diagram illustrating a method  300  for determining the confidence level  86  and the intent  88  of the specific remote vehicle  42 . Referring to  FIGS.  3  and  5 B , the method  300  may begin at decision block  302 . In decision block  302 , the confidence and intent module  62  determines a type of travel allowed by the current lane of travel for the specific remote vehicle  42 , where the type of travel includes through movement only and turns allowed. In response to determining the type of travel allowed by the current lane of travel is through movement only, then the method  300  may proceed to block  304 . 
     In block  304 , the confidence and intent module  62  sets the intent  88  as a connecting egress lane having a length expressed as an intent distance x, where x is expressed in meters. The intent distance is a distance measured along a travel path for the specific remote vehicle  42 , which is measured from a current position to the start of a predicted egress lane, plus a predetermined distance in the egress lane. It is to be appreciated that the intent distance includes a minimum length, which is specified a calibration parameter. The confidence and intent module  62  also sets an initial confidence level of the intent  88  as high, since it is clear that the specific remote vehicle  42  would normally continue to travel in the connecting egress lane since no turns are allowed. The method  300  may then proceed to decision block  308 , which is described below. 
     Returning back to decision block  302 , in response to determining the current lane of travel for the specific remote vehicle  42  allows for turns, then the method  300  may proceed to block  306 . In block  306 , the confidence and intent module  62  sets multiple values for the intent  88 , where each value corresponds to a length a potential connecting egress lane. The lengths are expressed as an intent distance x(i), where x is expressed in meters and i represents the number of potential connecting egress lanes. The confidence and intent module  62  also sets an initial confidence level of the intent  88  for each potential egress lane based on vehicle dynamics and any traffic light. For example, in an embodiment, the initial confidence level is a function of speed, acceleration, and the traffic light. Traffic lights may affect confidence levels in specific situations. For example, if a left turn lane currently has a red light but a straight lane has a green light, and if the specific remote vehicle  42  slows down when approaching the two traffic lights, then it is highly likely that the specific remote vehicle  42  plan to turn left. However, if the specific remote vehicle  42  does not slow down, then it is highly likely that the specific remote vehicle  42  plans to travel straight. The method  300  may then proceed to decision block  308 . 
     In decision block  308 , then compares the initial confidence level determined at either block  304  or  306  with the cached location information  92  from the vehicle database  90 , where the cached location information indicates previously calculated confidence levels. The confidence and intent module  62  compares the initial confidence level with the previously calculated confidence levels. In response to determining the initial confidence level is greater than or equal to the previously calculated confidence levels, then the method  300  proceeds to block  310 . However, in response to determining the initial confidence level is equal to or less than the previously calculated confidence levels, then the method  300  proceeds to block  312 . 
     In block  310 , the confidence and intent module  62  increases the initial confidence level by a predetermined value, and then sets the confidence level  86  to the initial confidence level. The method  200  may then terminate. 
     In block  312 , the confidence and intent module  62  sets the confidence level  86  to the initial confidence value. The method  200  may then terminate. 
       FIG.  6    is a process flow diagram illustrating a method  400  for determining the context  84  and the intent  88  for the specific remote vehicle  42  shown in  FIG.  2   . Referring generally to  FIGS.  1 ,  2 ,  3 , and  6   , the method  200  may begin at block  402 . In block  402 , the tracking and detection module  50  receives the cooperative infrastructure sensing messages  46 , which include sensed perception data from a perception device such as an infrastructure camera. The method  400  may then proceed to block  404 . 
     In block  404 , the tracking and detection module  50  of the controller  20  determines the plurality of vehicle parameters  68  related to the specific remote vehicle  42  based on the sensed perception data from the perception device. As mentioned above, the plurality of vehicle parameters  68  indicate a position, location geometry, detection time, and identifier of the specific remote vehicle  42  in addition to image data  64  collected by the remote infrastructure  44  (i.e., the red light cameras seen in  FIG.  2   ). The method  400  may then proceed to block  406 . 
     In block  406 , the noise modeling module  56  of the controller  20  converts the plurality of world coordinate pairs X, Y (seen in  FIG.  3   ) into image frame coordinates based on the homography matrix mentioned above. The method  400  may then proceed to block  408 . 
     In block  408 , the Kalman filter determines the plurality of error resilient vehicle parameters  78  related to the specific remote vehicle  42  based on the noise associated with converting the world coordinate pairs X, Y into image frame coordinates. It is to be appreciated that in some embodiments, block  408  may be omitted, and instead the plurality of vehicle parameters  68 , which have not been adjusted based on the noise associated with converting the world coordinate pairs X, Y into image frame coordinates. The method  400  may then proceed to block  410 . 
     In block  410 , the localization and map matching module  58  of the controller  20  associates the specific remote vehicle  42  with the specific lane of travel of the roadway based on the map data  80  from the road geometry database  82 . The method  400  may then proceed to block  412 . 
     In block  412 , the localization and map matching module  58  of the controller  20  determines the possible maneuvers, the possible egress lanes for the specific remote vehicle  42 , and the speed limit for the specific remote vehicle  42  for the specific lane of travel based on the map data  80  from the road geometry database  82 . The method  400  may then proceed to block  414 . 
     In block  414 , the context module  60  of the controller  20  determines the context  84  of the specific remote vehicle  42  based on the plurality of error resilient vehicle parameters  78 , the possible maneuvers, the possible egress lanes for the specific remote vehicle  42 , and the speed limit related to the specific remote vehicle  42 . The method  200  may then proceed to block  416 . 
     In block  416 , the confidence and intent module  62  of the controller  20  determines the confidence level  86  and the intent  88  of the specific remote vehicle  42  based on the plurality of error resilient vehicle parameters  78 , the possible maneuvers, the possible egress lanes for the specific remote vehicle  42 , and the speed limit related to the specific remote vehicle  42 . The method  400  may then terminate. 
     Referring generally to the figures, the disclosed communication system provides various technical effects and benefits. Specifically, the communication system provides an approach for determining a context and an intent for a remotely located vehicle when none is available based on presently available information. 
     The controllers may refer to, or be part of an electronic circuit, a combinational logic circuit, a field programmable gate array (FPGA), a processor (shared, dedicated, or group) that executes code, or a combination of some or all of the above, such as in a system-on-chip. Additionally, the controllers may be microprocessor-based such as a computer having a at least one processor, memory (RAM and/or ROM), and associated input and output buses. The processor may operate under the control of an operating system that resides in memory. The operating system may manage computer resources so that computer program code embodied as one or more computer software applications, such as an application residing in memory, may have instructions executed by the processor. In an alternative embodiment, the processor may execute the application directly, in which case the operating system may be omitted. 
     The description of the present disclosure is merely exemplary in nature and variations that do not depart from the gist of the present disclosure are intended to be within the scope of the present disclosure. Such variations are not to be regarded as a departure from the spirit and scope of the present disclosure.