Design and Evaluation of Remote Driving Architecture on 4G and 5G Mobile Networks - den Ouden, Jos; Ho, Victor; van der Smagt, Tijs; Kakes, Geerd; Rommel, Simon; Passchier, Igor; Juza, Jakub; Tafur Monroy, Idelfonso (Frontiers in Future Transportation, 2022)
Despite the progress in the development of automated vehicles in the last decade, reaching the level of reliability required at large scale deployment at an economical price and combined with safety requirements, is still a long road ahead. In certain use cases, such as automated shuttles and taxis, where there is no longer even a steering wheel and pedals required, remote driving could be implemented to bridge this gap; a remote operator can take control of the vehicle in situations where it is too difficult for an automated system to determine next actions. In logistics, it could even be implemented to solve already more pressing issues such as shortage of truck drivers, by providing a more flexible working conditions and less standstill time of the truck. An important aspect for remote driving is the connection between the remote station and the vehicle. With current roll-out of 5G mobile technology in many countries throughout the world, the implementation of remote driving comes closer to large scale deployment. 5G could be a potential game changer in deployment of this technology. In this work, we examine the remote driving application and network level performance of remote driving on a recently deployed sub-6GHz commercial 5G stand alone (SA) mobile network. It evaluates the influence of the 5G architecture, such as mobile edge computing (MEC) integration, local breakout and latency on the application performance of remote driving. We describe the design, development (based on Hardware-in-the-Loop simulations) and performance evaluation of a remote driving solution, tested on both 5G and 4G mobile SA networks using two different vehicles and two different remote stations. Two test cases have been defined to evaluate the application and network performance and are evaluated based on position accuracy, relative reaction times and distance perception. Results show the performance of the network to be sufficient for remote driving application at relatively low speeds (<40km/h). Network latencies compared to 4G, have dropped to half. A strong correlation between latency and remote driving performance is not clearly seen and requires further evaluation taking into account the influence of the user interface.