Generation and update of HD maps using data from heterogeneous sources

A system, comprising:

• one or more processors and one or more computer-readable non-transitory storage media in communication with the one or more processors, the one or more computer-readable non-transitory storage media comprising instructions operable when executed by the one or more processors to cause the system to perform operations comprising:
• accessing a plurality of training samples from a training data set, wherein the plurality of training samples comprises at least (1) a first sensor data sample including first sensor data, generated using a first sensor, of a first geographic location and first contextual information relating to the first sensor data as generated by the first sensor, and (2) a second sensor data sample including second sensor data, generated using a second sensor, of the first geographic location and second contextual information relating to the second sensor data as generated by the second sensor, wherein the first sensor is different from the second sensor, and wherein each of the first sensor data sample and the second sensor data sample comprises a known representation of the first geographic location;
• accessing target map data associated with the first geographic location, wherein the target map data is based on an existing high-definition (HD) map;
• subsequent to accessing the plurality of training samples and accessing the target map data, training a trainable model by:
• generating first map data by using the trainable model to encode the first sensor data sample and the second sensor data sample into a first latent representation in a common data space;
• and
• updating the trainable model by comparing the generated first map data and the target map data;
• and
• subsequent to determining that the training of the trainable model is complete:
• generating an updated HD map by utilizing the trained trainable model based at least in part on the existing HD map by:
• accessing a third sensor data sample including third sensor data, generated using a third sensor, of a second geographic location and third contextual information relating to the third sensor data as generated by the third sensor;
• and
• generating second map data associated with the second geographic location by using the trained trainable model to encode the third sensor data and the third contextual information into a second latent representation in the common data space.

One or more computer-readable non-transitory storage media including instructions that are operable when executed to cause one or more processors to perform operations comprising:

• accessing a plurality of training samples from a training data set, wherein the plurality of training samples comprises at least (1) a first sensor data sample including first sensor data, generated using a first sensor, of a first geographic location and first contextual information relating to the first sensor data as generated by the first sensor, and (2) a second sensor data sample including second sensor data, generated using a second sensor, of the first geographic location and second contextual information relating to the second sensor data as generated by the second sensor, wherein the first sensor is different from the second sensor, and wherein each of the first sensor data sample and the second sensor data sample comprises a known representation of the first geographic location;
• accessing target map data associated with the first geographic location, wherein the target map data is based on an existing high-definition (HD) map;
• subsequent to accessing the plurality of training samples and accessing the target map data, training a trainable model by:
• generating first map data by using the trainable model to encode the first sensor data sample and the second sensor data sample into a first latent representation in a common data space;
• and
• updating the trainable model by comparing the generated first map data and the target map data;
• and
• subsequent to determining that the training of the trainable model is complete:
• generating an updated HD map by utilizing the trained trainable model based at least in part on the existing HD map by:
• accessing a third sensor data sample including third sensor data, generated using a third sensor, of a second geographic location and third contextual information relating to the third sensor data as generated by the third sensor;
• and
• generating second map data associated with the second geographic location by using the trained trainable model to encode the third sensor data and the third contextual information into a second latent representation in the common data space.