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.

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.

In autonomous cars accurate and reliable detection of objects in the proximity of the vehicle is necessary in order to perform further safety critical actions which depend upon it. Many detectors have been developed in the last few years, but there is still demand for more reliable and more robust detectors. Some detectors rely on a single sensor, while some others are based upon fusion of data from multiple sources. The main aim of this paper is to show how image features can contribute to performance improvement of detectors which rely on pointcloud data only. In addition it will be shown, how lidar reflectance data can be substituted by low level image features without degrading the performance of detectors. Three different approaches are proposed to fuse image features with point cloud data. The extended networks are compared with the original network and tested on a well-known dataset and on our own data, as well. This might be important when the same pretrained model is to be used on data generated by a lidar using different reflectance encoding schemes and when due to the lack of training data retraining is not possible. Different augmentation techniques have been proposed and tested on the KITTI dataset as well as on data acquired by a different lidar sensor. The networks augmented with image features achieved a recall increase of a few percent for occluded objects.

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.

UN Regulation No. 157 was the first to establish worldwide uniform provisions for the type-approval of SAE Level 3 (L3) Automated Driving Systems (ADS) called Automated Lane Keeping Systems (ALKS). To ensure the safety of the systems, detailed test provisions were introduced, which include mandatory public road tests. The present work is focusing on public road testing and safety assessment of ALKS systems and reports on the methodology, challenges and lessons learned from a proof-of-concept experimental campaign. In line with the regulatory requirements concerning the minimum duration of system verification on public roads, various traffic circumstances and mandatory traffic scenarios were all considered in carrying out the experimental campaign. Since there was no suitable ADS on the market, a vehicle equipped with an SAE L2 system was used instead. The effort was aimed at providing a practical proof of concept for the regulatory provisions and not verifying the compliance of the vehicle under test. By carefully planning test execution, the authors managed to encounter the required traffic density conditions in all needed traffic scenarios with their necessary repetition number, within four testing days. In conclusion, the experimental campaign put in place under UN R157 proved to be a feasible exercise.