Abstract: The disclosed technology provides solutions for facilitating automated cleaning of an autonomous vehicle (AV) and in particular, for identifying objects and maintenance areas within an AV cabin. A process of the disclosed technology can include steps for collecting sensor data representing a cabin of an autonomous vehicle (AV) using an optical sensor disposed on a robotic arm, identifying one or more objects represented by the sensor data, and determining if the cabin of the AV can be cleaned using one or more tools associated with the robotic arm based on an identification of at least one of the one or more objects. Systems and machine-readable media are also provided.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method of predicting sensor information of an autonomous vehicle includes: receiving point cloud data sensed from an environment associated with the vehicle; processing, by a processor, point cloud data with a convolutional neural network (CNN) to produce a set of segmentations stored in a set of memory cells; processing, by the processor, the set of segmentations of the CNN with a recurrent neural network (RNN) to predict future point cloud data; processing, by the processor, the future point cloud data to determine an action; and controlling the vehicle based on the action.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method of predicting traffic patterns includes providing, within an autonomous vehicle, a first set of prediction policies. The method further includes receiving traffic pattern data associated with an object observed by the autonomous vehicle, the traffic pattern data including a kinematic estimate for the object, a position sequence for the object, and road semantics associated with a region near the object. A predicted path for the object is determined based on the first set of prediction policies and the traffic pattern data, and an actual path for the object is determined. A new prediction policy for the object is determined if the difference between the predicted path and the actual path is above a predetermined threshold. A second set of prediction policies is produced based on the first set of prediction policies and the new policy.