Handling Road Marking Changes

1 . A method, comprising:

identifying, by one or more computing devices, a road marking in a road trajectory of a vehicle using an artificial intelligence (AI) model and sensor data from a sensor of the vehicle, wherein the sensor data includes two or more sensor modalities; and

performing, by the one or more computing devices, a map update based on a determination that there is a change in road markings in the road trajectory, wherein the determination is based on at least on-board data generated when the vehicle is traversing the road trajectory and off-board data generated using stored data.

2 . The method of claim 1 , further comprising:

detecting, by the one or more computing devices, the change based on an intersection over union (IoU) between a first road marking segmentation based on the off-board data and a second road marking segmentation based on the on-board data.

3 . The method of claim 1 , wherein the AI model is a Siamese network, the method further comprising:

detecting, by the one or more computing devices, the change based on a comparison by the Siamese network between the on-board data and the off-board data.

4 . The method of claim 1 , wherein the AI model comprises a first model and a second model, the method further comprising:

obtaining, by the one or more computing devices, a first road marking segmentation using the first model based on the off-board data; and

obtaining, by the one or more computing devices, a second road marking segmentation using the second model based on the on-board data.

5 . The method of claim 4 , further comprising:

training, by the one or more computing devices, the first model using a conditional random field (CRF) technique using the off-board data; and

training, by the one or more computing devices, the second model using ground truth data obtained from the first model.

6 . The method of claim 1 , further comprising:

classifying, by the one or more computing devices, the change based on detection of a degraded road marking, a new road marking, or a change in a lane representation.

7 . The method of claim 1 , wherein the sensor data comprises a LiDAR sweep and an image.

8 . The method of claim 1 , wherein the performing further comprises:

altering, by the one or more computing devices, a motion plan of the vehicle based on the change.

9 . The method of claim 1 , wherein the performing further comprises:

updating, by the one or more computing devices, a base map based on a determination that the change is permanent; and

propagating, by the one or more computing devices, an instruction to one or more vehicles to apply an update to the base map.

10 . A system, comprising:

a memory; and

at least one processor coupled to the memory and configured to perform operations comprising:

identifying a road marking in a road trajectory of a vehicle using an artificial intelligence (AI) model and sensor data from a sensor of the vehicle, wherein the sensor data includes two or more sensor modalities; and

performing a map update based on a determination that there is a change in road markings in the road trajectory, wherein the determination is based on at least on-board data generated when the vehicle is traversing the road trajectory and off-board data generated using stored data.

11 . The system of claim 10 , the operations further comprising:

detecting the change based on an intersection over union (IoU) between a first road marking segmentation based on the off-board data and a second road marking segmentation based on the on-onboard data.

12 . The system of claim 10 , wherein the AI model is a Siamese network, the operations further comprising:

detecting the change based on a comparison by the Siamese network between the on-board data and the off-board data.

13 . The system of claim 10 , wherein the AI model comprises a first model and a second model, and the operations further comprising:

obtaining a first road marking segmentation using the first model based on the off-board data; and

obtaining a second road marking segmentation using the second model based on the on-board data.

14 . The system of claim 13 , the operations further comprising:

training the first model using a conditional random field (CRF) technique using the off-board data; and

training the second model using ground truth data obtained from the first model.

15 . The system of claim 10 , the operations further comprising:

classifying the change based on a detection of a degraded road marking, a new road marking, or a change in a lane representation.

16 . The system of claim 10 , wherein the sensor data comprises a LiDAR sweep and an image.

17 . The system of claim 10 , the operations further comprising:

altering a motion plan of the vehicle based on the change.

18 . The system of claim 11 , the operations further comprising:

updating a base map based on a determination that the change is permanent; and

propagating an instruction to apply an update to the base map to one or more vehicles.

19 . A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

identifying a road marking in a road trajectory of a vehicle using an artificial intelligence (AI) model and sensor data from a sensor of the vehicle, wherein the sensor data includes two or more sensor modalities; and

performing a map update based on a determination that there is a change in road markings in the road trajectory, wherein the determination is based on at least on-board data generated when the vehicle is traversing the road trajectory and off-board data generated using stored data.

20 . The non-transitory computer-readable medium of claim 19 , the operations further comprising:

detecting the change based on an intersection over union (IoU) between a first road marking segmentation based on the off-board data and a second road marking segmentation based on the on-board data.