Abstract: A method for providing vehicle information includes: receiving first vehicle data encoded according to a first protocol and corresponding to an environment external to a vehicle; receiving high definition mapping data corresponding to objects in the environment external to the vehicle; generating position information for objects indicated in the high definition mapping data by correlating locations of objects indicated by the high definition mapping data with objects in the environment external to the vehicle detected by at least one sensor; generating second vehicle data by correlating the high definition mapping data, the position information, and the first vehicle data; and encoding the second vehicle data according to a second protocol.

Abstract: A method for vehicle lane assignment includes identifying lane boundaries for one or more lanes of a road being traversed by the vehicle. The method also includes determining a center-line on a per lane basis for the one or more lanes. For a lane, the method uses the respective lane boundaries to determine the respective center-line of the lane. The method also includes determining a lane width on a per lane basis for the one or more lanes. For a lane, the method uses the respective lane boundaries to determine the respective lane width of the lane. The method also includes calculating a first probability that the vehicle is within a first lane of the one or more lanes using the center-line and the lane width corresponding to the first lane. The method also includes determining whether the first probability is greater than a threshold. The method also includes, in response to a determination that the first probability is greater than the threshold, assigning the vehicle to the first lane.

Abstract: A method for determining a current state vector describing location and heading of an ego-vehicle with respect to a lane boundary of a road comprises a step of obtaining road sensor data from at least one road sensor of the ego-vehicle detecting the lane boundaries of the road. In another step, a measured state vector of the ego-vehicle is calculated from the road sensor data. Furthermore, motion state data related to current heading and velocity of the ego-vehicle is obtained and a predicted state vector of the ego-vehicle is calculated based on the motion state data of the ego-vehicle and a previous state vector of the ego-vehicle. Finally a current state vector is determined by calculating a weighted average of the measured state vector and the predicted state vector of the ego-vehicle. The weights are determined based on characteristics of an upcoming section of the road.