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Cooperation of automated vehicles (AVs) can improve safety, efficiency andcomfort in traffic. Digital twins of Cooperative Intelligent Transport Systems(C-ITS) play an important role in monitoring, managing and improving traffic.Computing a live digital twin of traffic requires as input live perception dataof preferably multiple connected entities such as automated vehicles (AVs). Onesuch type of perception data are evidential occupancy grid maps (OGMs). Thecomputation of a digital twin involves their spatiotemporal alignment andfusion. In this work, we focus on the spatial alignment, also known asregistration, and fusion of evidential occupancy grid maps of multipleautomated vehicles. While there exists extensive research on thesynchronization and fusion of object-based environment representations, theregistration and fusion of OGMs originating from multiple connected vehicleshas not been investigated much. We propose a methodology that involves traininga deep neural network (DNN) to predict a fused evidential OGM from two OGMscomputed by different AVs. The output includes an estimate of the first- andsecond-order uncertainty. We demonstrate that the DNN trained with syntheticdata only outperforms a baseline approach based on coordinate transformationand combination rules also on real-world data. Experimental results onsynthetic data show that our approach is able to compensate for spatialmisalignments of up to 5 meters and 20 degrees.

Combined Registration and Fusion of Evidential Occupancy Grid Maps for Live Digital Twins of Traffic