Abstract: Various technologies described herein pertain to labeling sensor data generated by autonomous vehicles. A computing device identifies candidate path plans for an object in a driving environment of an autonomous vehicle based upon sensor data generated by sensor systems of the autonomous vehicle. The sensor data is indicative of positions of the object in the driving environment at sequential timesteps in a time period. Each candidate path plan is indicative of a possible maneuver being executed by the object during the time period. The computing device generates a weighted directed graph based upon the candidate path plans. The computing device determines a shortest path through the weighted directed graph. The computing device assigns a maneuver label to the sensor data based upon the shortest path, wherein the maneuver label is indicative of a maneuver that the object executes during the time period.

Abstract: Various technologies described herein pertain to labeling sensor data generated by autonomous vehicles. A computing device identifies candidate path plans for an object in a driving environment of an autonomous vehicle based upon sensor data generated by sensor systems of the autonomous vehicle. The sensor data is indicative of positions of the object in the driving environment at sequential timesteps in a time period. Each candidate path plan is indicative of a possible maneuver being executed by the object during the time period. The computing device generates a weighted directed graph based upon the candidate path plans. The computing device determines a shortest path through the weighted directed graph. The computing device assigns a maneuver label to the sensor data based upon the shortest path, wherein the maneuver label is indicative of a maneuver that the object executes during the time period.

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.