Abstract: A system and method for monitoring a road segment includes determining a geographic position of a vehicle in context of a digitized roadway map. A perceived point cloud and a mapped point cloud associated with the road segment are determined. An error vector is determined based upon a transformation between the mapped point cloud and the perceived point cloud. A first confidence interval is derived from a Gaussian process that is composed from past observations. A second confidence interval associated with a longitudinal dimension and a third confidence interval associated with a lateral dimension are determined based upon the mapped point cloud and the perceived point cloud. A Kalman filter analysis is executed to dynamically determine a position of the vehicle relative to the roadway map based upon the error vector, the first confidence interval, the second confidence interval, and the third confidence interval.

Abstract: In various embodiments, methods and systems are provided for controlling charging for a plurality of electric vehicles, each of the plurality of electric vehicles including a respective battery that is charged electrically via a respective charger. In one exemplary embodiment: (i) vehicle data, including respective charge statuses for the respective batteries of the plurality of electric vehicles, via sensors onboard the plurality of electric vehicles; (ii) charging station data, including respective availabilities for the plurality of charging stations, is obtained; (iii) the plurality of electric vehicles are each paired with respective charging stations, generated paired sets of electric vehicles and charging stations, based on the vehicle data and the charging station data, via a processor; and (iv) charging of respective batteries of the electric vehicles are controlled with the respective paired charging stations, via the processor.

Abstract: A system and method for monitoring a road segment includes determining a geographic position of a vehicle in context of a digitized roadway map. A perceived point cloud and a mapped point cloud associated with the road segment are determined. An error vector is determined based upon a transformation between the mapped point cloud and the perceived point cloud. A first confidence interval is derived from a Gaussian process that is composed from past observations. A second confidence interval associated with a longitudinal dimension and a third confidence interval associated with a lateral dimension are determined based upon the mapped point cloud and the perceived point cloud. A Kalman filter analysis is executed to dynamically determine a position of the vehicle relative to the roadway map based upon the error vector, the first confidence interval, the second confidence interval, and the third confidence interval.

Abstract: In various embodiments, methods and systems are provided for controlling charging for a plurality of electric vehicles, each of the plurality of electric vehicles including a respective battery that is charged electrically via a respective charger. In one exemplary embodiment: (i) vehicle data, including respective charge statuses for the respective batteries of the plurality of electric vehicles, via sensors onboard the plurality of electric vehicles; (ii) charging station data, including respective availabilities for the plurality of charging stations, is obtained; (iii) the plurality of electric vehicles are each paired with respective charging stations, generated paired sets of electric vehicles and charging stations, based on the vehicle data and the charging station data, via a processor; and (iv) charging of respective batteries of the electric vehicles are controlled with the respective paired charging stations, via the processor.