Selecting Remote Driving Locations for Latency Sensitive Reliable Tele-Operation

Abstract

These days, autonomous vehicles (AVs) technology has been improved dramatically. However, even though the AVs require no human intervention in most situations, AVs may fail in certain situations. In such cases, it is desirable that humans can operate the vehicle manually to recover from a failure situation through remote driving. Furthermore, we believe that remote driving can enhance the current transportation system in various ways. In this paper, we consider a revolutionary transportation platform, where all the vehicles in an area are controlled by some remote controllers or drivers so that transportation can be performed in a more efficient way. For example, road capacity can be effectively utilized and fuel efficiency can be increased by centralized remote control. However, one of the biggest challenges in such remote driving is the communication latency between the remote driver and the vehicle. Thus, selecting appropriate locations of the remote drivers is very important to avoid any type of safety problem that might happen due to large communication latency. Furthermore, the selection should reflect the traffic situation created by multiple vehicles in an area. To tackle these challenges, in this paper, we propose several algorithms that select remote drivers’ locations for a given transportation schedules of multiple vehicles. We consider two objectives in this system and evaluate the performance of the proposed algorithms through simulations. The results show that the proposed algorithms perform better than some baseline algorithms.

Exploration into the Needs and Requirements of the Remote Driver When Teleoperating the 5G-Enabled Level 4 Automated Vehicle in the Real World—A Case Study of 5G Connected and Automated Logistics

“…Moreover, Bogdoll et al [ 20 ] conducted a survey on the terminologies of remote human input systems for automated driving and proposed a taxonomy which potentially provides clarity and reduces confusion in the field of the teleoperation of vehicle automation. Finally, Zulqarnain et al [ 21 ] proposed algorithms to solve the largest challenge of teleoperation systems in automated vehicles—that is, the latency between the teleportation workstation where the remote drivers are located and the automated vehicles. Their research potentially enlightens the selection of suitable locations for teleoperation workstations.…”

Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments

“…Low-latency real-time transmission: Achieving an average end-to-end latency of approximately 20 ms is crucial for ensuring prompt remote monitoring [22].…”