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Highly automated vehicles rely on high-definition maps to ensure both safety and comfort of their passengers while driving. The maps provide a centimetre-accurate representation of the surrounding infrastructure to the car and ease tasks like localisation and object recognition by providing comparable reference material. Maintaining the maps with updates for the current traffic situation (e.g.traffic jams or construction work) is a challenging task. In the German government-funded research project Cooperative Highly Automated Driving (Ko-HAF) it is investigated to what extend vehicle sensors can be used to update these kinds of maps. In this work we present our obtained results and investigate the requirements for the cellular network infrastructure required for highly automated driving. To the best of our knowledge this work is one of the first that provides this correlation between data requirements and network infrastructure capabilities.

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This article explores the utilization of the processing power of GPUs using CUDA computation for real-time aggregation of multi-sensor data and detection of 3D objects using parallel clustering algorithms. The purpose is to implement an algorithm that fuses raw lidar point cloud data and 2D camera image object detections to produce 3D object clusters in a lidar point cloud. Most of the computation has been implemented using CUDA parallelism to investigate the capability of GPU devices in this task, which is a common challenge in automated driving. The results indicate that processing times can be optimized within the algorithm, which is crucial when considering the large amounts of data provided by lidar and camera-based systems. The algorithm can perform inference on the Jetson Xavier AGX at rates of ~20 to ~220 ms depending on the number of objects and their corresponding point amounts in the KITTI dataset.