A spectacular measurement campaign was carried out on a real-world motorway stretch of Hungary with the participation of international industrial and academic partners. The measurement resulted in vehicle based and infrastructure based sensor data that will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles—equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization—carried out special test scenarios while collecting detailed data using different sensors. All of the test runs were recorded by both vehicles and infrastructure. The paper also showcases application examples to demonstrate the viability of the collected data having access to the ground truth labeling. This data set may support a large variety of solutions, for the test and validation of different kinds of approaches and techniques. As a complementary task, the available 5G network was monitored and tested under different radio conditions to investigate the latency results for different measurement scenarios. A part of the measured data has been shared openly, such that interested automotive and academic parties may use it for their own purposes.

We demonstrate a working functional prototype of a cooperative perception system that maintains a real-time digital twin of the traffic environment, providing a more accurate and more reliable model than any of the participant subsystems—in this case, smart vehicles and infrastructure stations—would manage individually. The importance of such technology is that it can facilitate a spectrum of new derivative services, including cloud-assisted and cloud-controlled ADAS functions, dynamic map generation with analytics for traffic control and road infrastructure monitoring, a digital framework for operating vehicle testing grounds, logistics facilities, etc. In this paper, we constrain our discussion on the viability of the core concept and implement a system that provides a single service: the live visualization of our digital twin in a 3D simulation, which instantly and reliably matches the state of the real-world environment and showcases the advantages of real-time fusion of sensory data from various traffic participants. We envision this prototype system as part of a larger network of local information processing and integration nodes, i.e., the logically centralized digital twin is maintained in a physically distributed edge cloud.

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The paper presents the measurement campaign carried out on a real-world motorway stretch of Hungary with the participation of both industrial and academic partners from Austria and Hungary. The measurement included vehicle based as well as infrastructure based sensor data. The obtained results will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles - equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization - carried out special test scenarios while collecting detailed data using different sensors. All test runs were recorded by both vehicles and infrastructure. As a complementary task, the available 5G network was monitored and tested. The paper also showcases application examples based on the measurement campaign data, in which the added value of having access to the ground truth labeling and the created UHD map of the motorway section becomes apparent. In order to present our work transparently, a part of the measured data have been shared openly such that interested automotive as well as academic parties may use it for their own purposes.

Environment perception plays a significant role in autonomous driving since all traffic participants in the vehicle’s surroundings must be reliably recognized and localized in order to take any subsequent action. The main goal of this paper is to present a neural network approach for fusing camera images and LiDAR point clouds in order to detect traffic participants in the vehicle’s surroundings more reliably. Our approach primarily addresses the problem of sparse LiDAR data (point clouds of distant objects), where due to sparsity the point cloud based detection might become ambiguous. In the proposed model each 3D point in the LiDAR point cloud is augmented by semantically strong image features allowing us to inject additional information for the network to learn from. Experimental results show that our method increases the number of correctly detected 3D bounding boxes in sparse point clouds by at least 13–21 % and thus raw sensor fusion is validated as a viable approach for enhancing autonomous driving safety in difficult sensory conditions.