Abstract: An autonomous vehicle includes one or more sensors for detecting an object in an environment surrounding the autonomous vehicle and a vehicle computing system comprising one or more processors receiving canonical route data associated with at least one canonical route, and controlling travel of the autonomous vehicle based on sensor data from the one or more sensors and the canonical route data associated with the at least one canonical route. The at least one canonical route comprises at least one roadway connected with another roadway in a plurality of roadways in a geographic location that satisfies at least one route optimization function derived based on trip data associated with one or more traversals of the plurality of roadways in a geographic location by one or more autonomous vehicles.

Abstract: An autonomous vehicle includes one or more sensors for detecting an object in an environment surrounding the autonomous vehicle and a vehicle computing system comprising one or more processors receiving canonical route data associated with at least one canonical route, and controlling travel of the autonomous vehicle based on sensor data from the one or more sensors and the canonical route data associated with the at least one canonical route. The at least one canonical route comprises at least one roadway connected with another roadway in a plurality of roadways in a geographic location that satisfies at least one route optimization function derived based on trip data associated with one or more traversals of the plurality of roadways in a geographic location by one or more autonomous vehicles.

Abstract: A method for determining a canonical route includes receiving trip data associated with one or more traversals of a plurality of roadways in a geographic location by one or more autonomous vehicles. The method includes generating at least one canonical route based on the trip data, wherein the at least one canonical route includes at least one roadway connected with another roadway in the plurality of roadways. The method includes providing canonical route data associated with the at least one canonical route to an autonomous vehicle for controlling travel of the autonomous vehicle on the at least one canonical route.

Abstract: A method for predicting one or more parking locations includes receiving feature map data associated with a feature map, the feature map comprises a plurality of elements of a matrix, each element of the matrix comprises the feature map data, and the feature map data is associated with one or more features of a road. The method includes processing the feature map data to produce artificial neuron data associated with one or more artificial neurons of one or more convolution layers. The method includes generating a prediction score for each element of the feature map based on the artificial neuron data, wherein the prediction score comprises a prediction of whether each element of the feature map comprises a parking location. The method includes outputting map data associated with a map, the map data is based on the one or more prediction scores associated with each element of the feature map.