GNSS deviation map layer

1 . A computer comprising a processor and a memory, the memory storing instructions executable by the processor to:

receive a positional deviation for a vehicle, the positional deviation based on sensor data generated by environmental sensors on board the vehicle, map data, and global navigation satellite system (GNSS) data received at the vehicle, the positional deviation indicating a difference between a GNSS pose of the vehicle derived from the GNSS data and a localized position of the vehicle indicated by the sensor data and the map data;

update a classification of a geographic area in a GNSS deviation map layer based on the positional deviation, the GNSS deviation map layer indicating a reliability of the GNSS data; and

select the classification from a preset plurality of potential classifications stored in the memory.

2 . The computer of claim 1 , wherein the instructions further include instructions to update the classification based on the positional deviation and based on a covariance of the GNSS data received at the vehicle.

3 . The computer of claim 2 , wherein the instructions further include instructions to select a first potential classification as the classification in response to the positional deviation exceeding a first threshold and the covariance being below a second threshold, and select a second potential classification as the classification in response to the positional deviation exceeding the first threshold and the covariance exceeding the second threshold.

4 . The computer of claim 1 , wherein the vehicle is a first vehicle of a plurality of vehicles, the positional deviation is a first positional deviation of a plurality of positional deviations of the respective vehicles, the classification is a first classification of a plurality of classifications of the GNSS deviation map layer, and the instructions further include instructions to update the classifications based on the positional deviations.

5 . The computer of claim 4 , wherein the geographic area is a first geographic area, the classifications include a second classification of a second geographic area that lacks any of the vehicles, and the instructions further include instructions to update the second classification by executing a machine-learning program that generates an output indicating an expected classification.

6 . The computer of claim 5 , wherein the instructions further include instructions to train the machine-learning program with the positional deviations as training data.

7 . The computer of claim 5 , wherein the instructions further include instructions to update the second classification by executing the machine-learning program in response to the second classification of the second geographic area before the update indicating that the reliability of the GNSS data exceeds a threshold in a direction indicating lower reliability.

8 . The computer of claim 4 , wherein the geographic area is a first geographic area, the classifications include a second classification of a second geographic area that lacks any of the vehicles, and the instructions further include instructions to maintain the second classification at the same value as before the update in response to the second classification of the second geographic area before the update indicating that the reliability of the GNSS data exceeds a threshold in a direction indicating higher reliability.

9 . The computer of claim 1 , wherein the instructions further include instructions to determine the positional deviation based on the sensor data, the map data, and the GNSS data.

10 . The computer of claim 9 , wherein the instructions further include instructions to determine the positional deviation by detecting features in the sensor data, the positional deviation being a difference between expected positions of the features based on the GNSS data and map positions of the features from the map data.

11 . The computer of claim 10 , wherein the instructions further include instructions to determine the positional deviation by determining the expected positions of the features based on the GNSS pose of the vehicle derived from the GNSS data.

12 . The computer of claim 11 , wherein the instructions further include instructions to determine the positional deviation by executing an optimization algorithm matching the expected positions to the map positions.

13 . The computer of claim 1 , wherein the potential classifications include at least one first potential classification indicating that the reliability is at least suitable for road-level position detection and at least one second potential classification indicating that the reliability is unsuitable for road-level position detection.

14 . The computer of claim 13 , wherein the potential classifications include at least one third potential classification indicating that the reliability is at least suitable for lane-level position detection.

15 . The computer of claim 1 , wherein the potential classifications include at least one first potential classification indicating that the positional deviation is above a threshold and at least one second potential classification indicating that the positional deviation is below the threshold.

16 . The computer of claim 15 , wherein the threshold is a first threshold, the at least one second potential classification indicates that the positional deviation is below the first threshold and above a second threshold, and the potential classifications include at least one third potential classification indicating that the positional deviation is below the second threshold.

17 . A method comprising:

receiving a positional deviation for a vehicle, the positional deviation based on sensor data generated by environmental sensors on board the vehicle, map data, and global navigation satellite system (GNSS) data received at the vehicle, the positional deviation indicating a difference between a GNSS pose of the vehicle derived from the GNSS data and a localized position of the vehicle indicated by the sensor data and the map data;

updating a classification of a geographic area in a GNSS deviation map layer based on the positional deviation, the GNSS deviation map layer indicating a reliability of the GNSS data; and

selecting the classification from a preset plurality of potential classifications stored in a memory.

18 . The method of claim 17 , further comprising determining the positional deviation by detecting features in the sensor data, the positional deviation indicating a difference between expected positions of the features based on the GNSS data and map positions of the features from the map data.

19 . The method of claim 18 , further comprising determining the positional deviation by determining the expected positions of the features based on the GNSS pose of the vehicle derived from the GNSS data.

20 . A computer comprising a processor and a memory, the memory storing instructions executable by the processor to:

receive a plurality of positional deviations for a plurality of respective vehicles including a first positional deviation of a first vehicle, the first positional deviation based on sensor data generated by environmental sensors on board the first vehicle, map data, and global navigation satellite system (GNSS) data received at the first vehicle, the first positional deviation indicating a difference between a GNSS pose of the first vehicle derived from the GNSS data and a localized position of the first vehicle indicated by the sensor data and the map data;

update a first classification of a first geographic area in a GNSS deviation map layer based on the first positional deviation, the GNSS deviation map layer indicating a reliability of the GNSS data, wherein classifications for the GNSS deviation map layer include the first classification and a second classification of a second geographic area that lacks any of the vehicles;

update the classifications based on the positional deviations; and

update the second classification by executing a machine-learning program that generates an output indicating an expected classification.