Determining localization confidence of vehicles based on convergence ranges

Abstract

According to an aspect of an embodiment, operations may comprise for each of the set of geographic X-positions, accessing an HD map of a geographical region surrounding the geographic X-position, determining a convergence range for the geographic X-position, and storing the convergence range for the geographic X-position in the HD map. The operations may also comprise accessing the HD map, predicting a next geographic X-position of a target vehicle, predicting a covariance of the predicted next geographic X-position, accessing the convergence range for the geographic X-position in the HD map closest to the predicted next geographic X-position, estimating a current geographic X-position of the target vehicle by performing a localization algorithm, and determining a confidence value for the estimated current geographic X-position of the target vehicle based on the predicted next geographic X-position, the predicted covariance, and the accessed convergence range.

What is claimed is: 
     
         1 . A computer-implemented method, comprising:
 accessing a set of geographic X-positions of one or more vehicles;   for each of the set of geographic X-positions:
 accessing a high definition (HD) map of a geographical region surrounding the geographic X-position, the HD map comprising a three-dimensional (3D) representation of the geographical region, 
 determining a convergence range for the geographic X-position, and 
 storing the convergence range for the geographic X-position in the HD map; 
   accessing the HD map;   predicting a next geographic X-position of a target vehicle;   predicting a covariance of the predicted next geographic X-position;   accessing the convergence range for the geographic X-position in the HD map closest to the predicted next geographic X-position;   estimating a current geographic X-position of the target vehicle by performing a localization algorithm; and   determining a confidence value for the estimated current geographic X-position of the target vehicle based on the predicted next geographic X-position, the predicted covariance, and the accessed convergence range.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein the confidence value for the estimated current geographic X-position of the target vehicle indicates a level of confidence that the target vehicle is actually located at the estimated current geographic X-position. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein the determining of the confidence value for the estimated current geographic X-position of the target vehicle comprises calculating an integral area between minimum and maximum values of the accessed convergence range of a Gaussian function of the predicted next geographic X-position. 
     
     
         4 . The computer-implemented method of  claim 1 , further comprising employing the convergence range along with a predicted uncertainty distribution for the estimated current geographic X-position to compute a new estimate of the confidence value for the estimated current geographic X-position that is used to update a Kalman Filter (KF). 
     
     
         5 . The computer-implemented method of  claim 1 , wherein the convergence range is specified relative to a convergence error with respect to each geographic X-position. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein multiple convergence ranges are stored in the HD map for each geographic X-position, with each of the multiple convergence ranges being relative to a specific convergence error tolerance, and with each convergence error tolerance configured to be used to compute confidence of a localization result relative to the convergence error tolerance. 
     
     
         7 . One or more non-transitory computer readable storage media storing instructions that in response to being executed by one or more processors, cause a computer system to perform operations, the operations comprising:
 accessing a set of geographic X-positions of one or more vehicles;   for each of the set of geographic X-positions:
 accessing a high definition (HD) map of a geographical region surrounding the geographic X-position, the HD map comprising a three-dimensional (3D) representation of the geographical region, 
 determining a convergence range for the geographic X-position, and 
 storing the convergence range for the geographic X-position in the HD map; 
   accessing the HD map;   predicting a next geographic X-position of a target vehicle;   predicting a covariance of the predicted next geographic X-position;   accessing the convergence range for the geographic X-position in the HD map closest to the predicted next geographic X-position;   estimating a current geographic X-position of the target vehicle by performing a localization algorithm; and   determining a confidence value for the estimated current geographic X-position of the target vehicle based on the predicted next geographic X-position, the predicted covariance, and the accessed convergence range.   
     
     
         8 . The one or more non-transitory computer-readable storage media of  claim 7 , wherein the confidence value for the estimated current geographic X-position of the target vehicle indicates a level of confidence that the target vehicle is actually located at the estimated current geographic X-position. 
     
     
         9 . The one or more non-transitory computer-readable storage media of  claim 7 , wherein the determining of the confidence value for the estimated current geographic X-position of the target vehicle comprises calculating an integral area between minimum and maximum values of the accessed convergence range of a Gaussian function of the predicted next geographic X-position. 
     
     
         10 . The one or more non-transitory computer-readable storage media of  claim 7 , wherein the operations further comprise employing the convergence range along with a predicted uncertainty distribution for the estimated current geographic X-position to compute a new estimate of the confidence value for the estimated current geographic X-position that is used to update a Kalman Filter (KF). 
     
     
         11 . The one or more non-transitory computer-readable storage media of  claim 7 , wherein the convergence range is specified relative to a convergence error with respect to each geographic X-position. 
     
     
         12 . The one or more non-transitory computer-readable storage media of  claim 7 , wherein multiple convergence ranges are stored in the HD map for each geographic X-position, with each of the multiple convergence ranges being relative to a specific convergence error tolerance, and with each convergence error tolerance configured to be used to compute confidence of a localization result relative to the convergence error tolerance. 
     
     
         13 . A computer system comprising:
 one or more processors; and   one or more non-transitory computer readable media storing instructions that in response to being executed by the one or more processors, cause the computer system to perform operations, the operations comprising:   accessing a set of geographic X-positions of one or more vehicles;   for each of the set of geographic X-positions:
 accessing a high definition (HD) map of a geographical region surrounding the geographic X-position, the HD map comprising a three-dimensional (3D) representation of the geographical region, 
 determining a convergence range for the geographic X-position, and 
 storing the convergence range for the geographic X-position in the HD map; 
   accessing the HD map;   predicting a next geographic X-position of a target vehicle;   predicting a covariance of the predicted next geographic X-position;   accessing the convergence range for the geographic X-position in the HD map closest to the predicted next geographic X-position;   estimating a current geographic X-position of the target vehicle by performing a localization algorithm; and   determining a confidence value for the estimated current geographic X-position of the target vehicle based on the predicted next geographic X-position, the predicted covariance, and the accessed convergence range.   
     
     
         14 . The computer system of  claim 13 , wherein the confidence value for the estimated current geographic X-position of the target vehicle indicates a level of confidence that the target vehicle is actually located at the estimated current geographic X-position. 
     
     
         15 . The computer system of  claim 13 , wherein the determining of the confidence value for the estimated current geographic X-position of the target vehicle comprises calculating an integral area between minimum and maximum values of the accessed convergence range of a Gaussian function of the predicted next geographic X-position. 
     
     
         16 . The computer system of  claim 13 , wherein the operations further comprise employing the convergence range along with a predicted uncertainty distribution for the estimated current geographic X-position to compute a new estimate of the confidence value for the estimated current geographic X-position that is used to update a Kalman Filter (KF). 
     
     
         17 . The computer system of  claim 13 , wherein the convergence range is specified relative to a convergence error with respect to each geographic X-position. 
     
     
         18 . The computer system of  claim 13 , wherein multiple convergence ranges are stored in the HD map for each geographic X-position, with each of the multiple convergence ranges being relative to a specific convergence error tolerance, and with each convergence error tolerance configured to be used to compute confidence of a localization result relative to the convergence error tolerance.