Design and Evaluation of Remote Driving Architecture on 4G and 5G Mobile Networks

Abstract

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 the next actions. In logistics, it could even be implemented to solve already more pressing issues such as shortage of truck drivers, by providing more flexible working conditions and less standstill time of the truck. An important aspect of remote driving is the connection between the remote station and the vehicle. With the 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 the 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-6-GHz 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 applications at relatively low speeds (<40 km/h). Network latencies compared with 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.

Decentralised IOTA-Based Concepts of Digital Trust for Securing Remote Driving in an Urban Environment

“…For example, ref. [20] describes a remote driving architecture but states only that the vehicle and remote driving station use a secure login to a mediating gateway. Another architecture described in [21] focuses on communications, sensing, and AI control, but does not address driver authentication or authorisation at all.…”

A Survey on Remote Operation of Road Vehicles

“…The results from this study, beside assessing their compression algorithm favorably, show the feasibility of remote driving over 4G networks at speeds up to 20 km/h. In the full-sized vehicle category, we find the work in [64], which evaluates latency on 4G and 5G networks for remote driving use cases. The study uses two full-sized vehicles (Toyota Prius III and IV) running in a closed property -a parking lot close to a 5G deployment by Dutch telecommunications operator KPN -where remote drivers performed straight-line and slalom driving tasks.…”

“…These two studies, [64] and [69] differ in a core issue: test-driver selection. While the work in [64] uses voluntary non-professional drivers (following a trend that is observed in studies of human performance from Table 2), the work in [69] highlights their use of professional drivers (Japanese Class 2 license holders, which allows for driving public transportation vehicles). However, their participants do not have experience in remote driving situations.…”

“…However, it is only with fullsized prototypes that real world factors (e.g., braking distances, responses from mechanical systems) can be accounted for. The studies that use full-sized vehicles ( [64], [69]) are performed by or in association with corporate partners. The synergy between the industry and academia allows for better-equipped studies.…”

Outdoor mm-wave 5G/6G transmission with adaptive analog beamforming and IFoF fronthaul

“…In particular, this manuscript presents an experimental wireless IFoF testbed realizing different outdoor scenarios at a center frequency of 27 GHz, within the n257 and n258 5G bands 2 . The presented outdoor experiment is performed in a parking lot, serving as a measurement campaign for evaluation and validation of vehicle applications such as remote driving or cooperative, connected and automated mobility (CCAM) services 29 . In the experimental setup, a pair of PAA panels are employed on the transmitter and receiver sides, allowing for beam steering capabilities.…”