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.
Highlights
In recent years, significant advances have been made (Chan, 2017) on the development of different kinds of automated driving vehicles: small people movers, targeting public transport like services at relatively slow speeds; passenger cars, initially targeting taxi-like services, but potentially usable for private transportation; and industrial vehicles, targeting, e.g., dedicated use cases in harbors or mining operations
Remote driving could bridge this gap (Baraniuk, 2020) by allowing a remote operator to take over control over the vehicle in case it is too difficult for an automated vehicle to figure out what to do by itself
Remote driving can even be used to teach the artificial intelligence (AI) in the automated driving system how to maneuver in certain difficult situations, which humans are far more capable of handling at this time (Baraniuk, 2020)
Summary
Significant advances have been made (Chan, 2017) on the development of different kinds of automated driving vehicles: small people movers (shuttles), targeting public transport like services at relatively slow speeds; passenger cars, initially targeting taxi-like services, but potentially usable for private transportation; and industrial vehicles, targeting, e.g., dedicated use cases in harbors or mining operations. The control over the truck can be shifted to another remote driver after the first has reached the driving time limit, so the truck can move onward. It is already common practice to include a hand-held remote-control device with smaller cranes, loaders, excavators, and similar equipment (Dadhich et al, 2016), in order to allow the operator to control the machine from outside the cabin. This principle still requires the operator to be present on the operating site. Remote driving can even be used to teach the artificial intelligence (AI) in the automated driving system how to maneuver in certain difficult situations, which humans are far more capable of handling at this time (Baraniuk, 2020)
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