Abstract: Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a plurality of predicted trajectories of the object.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with the motion prediction and operation of a device including a vehicle are provided. For example, a vehicle computing system can access state data including information associated with locations and characteristics of objects over a plurality of time intervals. Trajectories of the objects at subsequent time intervals following the plurality of time intervals can be determined based on the state data and a machine-learned tracking and kinematics model. The trajectories of the objects can include predicted locations of the objects at subsequent time intervals that follow the plurality of time intervals. Further, the predicted locations of the objects can be based on physical constraints of the objects. Furthermore, indications, which can include visual indications, can be generated based on the predicted locations of the objects at the subsequent time intervals.

Abstract: Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a plurality of predicted trajectories of the object.

Abstract: Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a plurality of predicted trajectories of the object.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with the motion prediction and operation of a device including a vehicle are provided. For example, a vehicle computing system can access state data including information associated with locations and characteristics of objects over a plurality of time intervals. Trajectories of the objects at subsequent time intervals following the plurality of time intervals can be determined based on the state data and a machine-learned tracking and kinematics model. The trajectories of the objects can include predicted locations of the objects at subsequent time intervals that follow the plurality of time intervals. Further, the predicted locations of the objects can be based on physical constraints of the objects. Furthermore, indications, which can include visual indications, can be generated based on the predicted locations of the objects at the subsequent time intervals.

Abstract: Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a predicted trajectory of the object.

Abstract: A tracking server receives GPS data from a location tracking device located in a vehicle. The GPS data describes a path that is representative of a pathway of the vehicle used to complete a trip from a starting location to a destination location. The tracking server identifies noisy GPS data included in the received GPS data and revises a portion of path corresponding to the noisy GPS data. The tracking server may update a map database to include one or more road segments associated with the revised portion of the path. Furthermore, the tracking server may calculate a fare for the trip based on the revised path.

Abstract: A transport service system determines the accuracy of a map matched trajectory using a forward probability algorithm. A transport vehicle on a trip relays location data to the system. The system uses a map of the corresponding area and the location data to calculate an emission probability, the likelihood of a candidate road being associated with a location data point, and a transition probability, the likelihood of a second state occurring after a first state. The joint probability of the emission and transition probabilities is used to determine a total number of zero forward probability occurrences and an average forward probability associated with the trip. These metrics are used to measure the accuracy of the map matching algorithm for the trip.

Abstract: A transport service system determines the accuracy of a map matched trajectory using a forward probability algorithm. A transport vehicle on a trip relays location data to the system. The system uses a map of the corresponding area and the location data to calculate an emission probability, the likelihood of a candidate road being associated with a location data point, and a transition probability, the likelihood of a second state occurring after a first state. The joint probability of the emission and transition probabilities is used to determine a total number of zero forward probability occurrences and an average forward probability associated with the trip. These metrics are used to measure the accuracy of the map matching algorithm for the trip.

Abstract: A map server receives geographic points from a location tracking device located in a vehicle. The received geographic points describe a path that is representative of a pathway of the vehicle used to complete a trip from a starting location to a destination location. The map server identifies candidate geographic points for each received geographic point where each candidate geographic point is associated with a location on a known roadway. The map server determines a graph of the candidate geographic points and identifies different sub-graphs from the graph. The map server iteratively evaluates the sub-graphs to determine a shortest path from the starting location to the destination location without evaluating all the edges in the sub-graphs.

Abstract: Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a plurality of predicted trajectories of the object.

Abstract: Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a predicted trajectory of the object.

Abstract: A map server receives geographic points from a location tracking device located in a vehicle. The received geographic points describe a path that is representative of a pathway of the vehicle used to complete a trip from a starting location to a destination location. The map server identifies candidate geographic points for each received geographic point where each candidate geographic point is associated with a location on a known roadway. The map server determines a graph of the candidate geographic points and identifies different sub-graphs from the graph. The map server iteratively evaluates the sub-graphs to determine a shortest path from the starting location to the destination location without evaluating all the edges in the sub-graphs.

Abstract: A tracking server receives GPS data from a location tracking device located in a vehicle. The GPS data describes a path that is representative of a pathway of the vehicle used to complete a trip from a starting location to a destination location. The tracking server identifies noisy GPS data included in the received GPS data and revises a portion of path corresponding to the noisy GPS data. The tracking server may update a map database to include one or more road segments associated with the revised portion of the path. Furthermore, the tracking server may calculate a fare for the trip based on the revised path.

Abstract: A map server receives geographic points from a location tracking device located in a vehicle. The received geographic points describe a path that is representative of a pathway of the vehicle used to complete a trip from a starting location to a destination location. The map server identifies candidate geographic points for each received geographic point where each candidate geographic point is associated with a location on a known roadway. The map server determines a graph of the candidate geographic points and identifies different sub-graphs from the graph. The map server iteratively evaluates the sub-graphs to determine a shortest path from the starting location to the destination location without evaluating all the edges in the sub-graphs.

Abstract: A tracking server receives GPS data from a location tracking device located in a vehicle. The GPS data describes a path that is representative of a pathway of the vehicle used to complete a trip from a starting location to a destination location. The tracking server identifies noisy GPS data included in the received GPS data and revises a portion of path corresponding to the noisy GPS data. The tracking server may update a map database to include one or more road segments associated with the revised portion of the path. Furthermore, the tracking server may calculate a fare for the trip based on the revised path.

Abstract: A transport facilitation system can receive current location data from driver devices of drivers operating throughout a given region. The system can further receive a pick-up request from a user device of a requesting user within the given region, the pick-up request including pick-up location data. The system can determine a plurality of candidate drivers to service the pick-up request based, at least in part, on the pick-up location data and current location data of the candidate drivers. The system can predict a future location for each of the candidate drivers and select a first driver from the candidate drivers to service the pick-up request based on the predicted future locations. The system may then transmit a transport invitation to the first driver to service the pick-up request.

Abstract: A tracking server receives GPS data from a location tracking device located in a vehicle. The GPS data describes a path that is representative of a pathway of the vehicle used to complete a trip from a starting location to a destination location. The tracking server identifies noisy GPS data included in the received GPS data and revises a portion of path corresponding to the noisy GPS data. The tracking server may update a map database to include one or more road segments associated with the revised portion of the path. Furthermore, the tracking server may calculate a fare for the trip based on the revised path.