Collaborative Perception for Automated Vehicles Leveraging Vehicle-to-Vehicle Communications

Currently, many automated vehicle systems primarily perceive the environment from a single perspective and as a result are unable to leverage additional scene information from the viewpoint of other vehicles on the road using vehicleto-vehicle communication technologies. We study how increased data sharing can improve the perception capabilities of automated vehicles. Our methodology shares sensor measurements and objects detected by state-of-the-art deep learning networks between vehicles to increase the automated driving systems confidence of detecting objects using a 3D sensor fusion algorithm. This approach can benefit scenarios where an object may be occluded (fully or partially) or located too far away to classify accurately by a single vehicle alone.