1. A method for fusing outputs from multiple LiDAR sensors on a vehicle, said method comprising: detecting objects in proximity to the vehicle by the LiDAR sensors; providing object files for objects detected by the LiDAR sensors at a previous sample time, said object files having object models that identify a position, orientation and velocity of the detected objects by the sensors; tracking the object models of the objects detected by the sensors; projecting tracked object models in the object files from the previous sample time to provide predicted object models; receiving a plurality of scan returns from objects detected in a field-of-view of the sensors at a current sample time; constructing a scan point cloud from the scan returns; segmenting the scan points in the scan point cloud into predicted scan clusters where each scan cluster initially identifies an object detected by the sensors; matching the predicted scan clusters with the predicted object models; detecting objects in proximity to the vehicle by a radar sensor or a vision system; reading object data from objects detected by the radar sensor or the vision system; registering the object data to a coordinate frame designated for the scan points of the LiDAR sensors; matching, using a computer including a processor and memory, the object data with the object models in the object files using the predicted object models; merging predicted object models that have been identified as separate scan clusters in the previous sample time but are now identified as a single scan cluster in the current sample time using the matched predicted scan clusters and the matched object data; splitting predicted object models that have been identified as a single scan cluster in the previous sample time but are now identified as separate scan clusters in the current sample time using the matched predicted scan clusters and the matched object data; creating new object models for detected objects in the current sample time that were not present in the previous sample time using the matched predicted scan clusters and the matched object data; deleting object models that are no longer present in the predicted scan clusters using the matched predicted scan clusters and the matched object data; providing object model updates based on the merged object models, split object models, new object models and deleted object models; and updating the object files for the current sample time with the object model updates.