Determining localization confidence of vehicles based on convergence ranges

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.

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.