Viktor Tihanyi, András Rövid, Viktor Remeli, Zsolt Vincze, Mihály Csonthó, Zsombor Pethő, Mátyás Szalai, Balázs Varga, Aws Khalil, Zsolt Szalay
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.
Ádám Bárdos, Ádám Domina, Zsolt Szalay, Viktor Tihanyi, László Palkovics
IEEE Intelligent Vehicles Symposium (IV)
In the future, the presence of highly automated vehicles is expected to become more and more wide spread. In such systems, the whole driving task will be performed by the vehicle autonomously, thus, vehicles must be able to control their motion in various circumstances, even at stability limits. In this paper, the authors consider the control of a steady-state drifting maneuver, which means saturated rear tire forces. In a previous article, a MIMO linear quadratic regulator (LQR) controller was designed, and it showed good performance in simulation environment. The test results of a real vehicle implementation are presented here, which was the logical next step of the work. For the vehicle platform, a series production sports car was chosen. Modifications were made in order to enable by-wire control. After identifying the vehicle model parameters through measurements, the control algorithm was implemented on a rapid prototyping unit. Vehicle states were measured with a high precision dual antenna GNSS module with RTK correction. Additionally, other dynamic parameters from the vehicle CAN bus signals were also used. The main goal was to stabilize different drifting equilibria, which showed satisfying performance of the proposed controller in a real vehicle setup as well.
Testing self-driving vehicles is still a new and immature process; the globally harmonised procedure expected much later. The resource-demanding nature of real-world tests makes it indispensable to develop and improve the efficiency of virtual environment based testing methods. Accordingly, a novel X-in-the-Loop framework is proposed to fully exploit the recent advances in info-communication technologies, vehicle automation, and testing and validation requirements. This methodology real-time connects physical and virtual testing with high correlation while completely blurs the sharp boundaries between them. Measurement results confirm the superior performance of the 5G communication link in providing a stable, real-time connection between the real world and its virtual representation. The live demonstration proved the presented concept at the newly constructed Hungarian proving ground for automated driving. The performed investigation also includes comprehensive benchmarking, focusing on the most up-to-date automotive testing frameworks. The analysis considers the methodologies and techniques applied by the most relevant actors in the automotive testing sector worldwide. Accordingly, the newly developed testing framework is evaluated and validated in light of the state-of-the-art methods used by the automotive industry.