Cooperation of automated vehicles (AVs) can improve safety, efficiency and comfort 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 data of preferably multiple connected entities such as automated vehicles (AVs). One such type of perception data are evidential occupancy grid maps (OGMs). The computation of a digital twin involves their spatiotemporal alignment and fusion. In this work, we focus on the spatial alignment, also known as registration, and fusion of evidential occupancy grid maps of multiple automated vehicles. While there exists extensive research on the synchronization and fusion of object-based environment representations, the registration and fusion of OGMs originating from multiple connected vehicles has not been investigated much. We propose a methodology that involves training a deep neural network (DNN) to predict a fused evidential OGM from two OGMs computed by different AVs. The output includes an estimate of the first- and second-order uncertainty. We demonstrate that the DNN trained with synthetic data only outperforms a baseline approach based on coordinate transformation and combination rules also on real-world data. Experimental results on synthetic data show that our approach is able to compensate for spatial misalignments of up to 5 meters and 20 degrees.

翻译：自动驾驶车辆的协同可提升交通的安全性、效率与舒适性。协作式智能交通系统（C-ITS）的数字孪生在监测、管理和优化交通中发挥关键作用。计算交通的实时数字孪生需要输入来自多个联网实体（如自动驾驶车辆）的实时感知数据。其中一种感知数据类型为证据占用网格地图（OGM）。数字孪生的计算涉及其时空对齐与融合。本研究聚焦于多辆自动驾驶车辆证据占用网格地图的空间对齐（即配准）与融合。尽管基于对象的环境表征同步与融合已有大量研究，但源自多辆联网车辆的OGM配准与融合尚未得到充分探索。我们提出一种方法：训练深度神经网络（DNN）从不同自动驾驶车辆计算的两个OGM中预测融合后的证据OGM，其输出包含一阶与二阶不确定性估计。实验表明，仅使用合成数据训练的DNN在真实数据上的表现优于基于坐标变换与组合规则的基线方法。合成数据上的结果显示，该方法可补偿高达5米与20度的空间错位。