Distractions significantly delay remote drivers' reaction time

Level 4 automated vehicles can be controlled remotely by a specially trained remote driver using a teleoperation workstation. Led by Newcastle University, the study investigated how the remote drivers interact with self-driving vehicles in a real-world scenario. It focussed on the impacts of distraction and disengagement on driver performance.

The experts found that mental disengagement, achieved through a reading task distraction, significantly slowed the remote driver's reaction time by an average of 5.3 seconds when the driverless vehicle system required intervention, posing significant safety risks.

Published in the journal Electronics, the results also show that disengagement resulted in a 4.2 second delay in decision-making time for remote drivers when they needed to step in and make critical strategic decisions.

Serious safety issues

Level 4 automated vehicles, which this research was based on, are capable of automatically starting fail-safe and fail-operational protocols. One significant solution implemented as a fail-safe for Level 4 automated vehicles is the concept of remote driving. In this system, the automated vehicle can be controlled remotely by a specially trained remote driver using a teleoperation workstation.

The findings show that distraction and multitasking significantly increase response latency and impair the decision-making of the remote driver, potentially raising safety concerns.

Study lead author, Dr Shuo Li, Senior Research Associate at Newcastle University's School of Engineering, said: "The extended motor readiness time among remote drivers in the "disengaged" condition underscores the risks associated with driver distraction and reduced situational awareness, which could critically impair their ability to promptly assume control of the vehicle in situations that require immediate intervention.

"This highlights the importance of maintaining a certain level of cognitive readiness for remote drivers even when they are not controlling the vehicle remotely. Such delays could pose safety risks in real-world applications, where the timely execution of a 'GO' or 'NO GO' decision is essential for mitigating potential risks and ensuring smooth and cost-effective vehicle operation.

"In urgent scenarios where rapid intervention is required, even minor delays could potentially lead to serious safety issues. For the vehicle automation industry, this underscores the need to explore solutions and develop systems that minimise remote driver distractions and manage cognitive workload effectively. It also calls for improved human-machine interfaces and advanced driver warning systems to ensure that remote drivers can maintain optimal workload and situational awareness so that they can respond promptly and effectively."

Project V-CAL

This study investigated Level 4 automated vehicles powered by 5G technology, developed by a UK-based company specialising in vehicle automation. The vehicle was retrofitted from an existing Terberg electric tractor unit. The researchers' objective was to test and demonstrate the operational capabilities of a 5G-enabled autonomous delivery system in a real-world setting, focusing on the autonomous delivery of goods using a 40-tonne truck in North East England. The system they studied consists of a modified Terberg electric heavy goods vehicle (HGV) and a 5G-enabled teleoperation workstation.

The research was carried out as part of Project V-CAL. Led by the North East Automotive Alliance (NEAA), V-CAL will run up to four zero-emission autonomous HGVs between the Vantec and Nissan Sunderland sites, on private roads where the vehicles will navigate traffic lights, roundabouts, and other road users. This is a major step towards deploying the technology on public roads.

The work, in partnership with Newcastle University, Vantec, North East Automotive Alliance (NEAA), StreetDrone (Now part of Oxa), Nissan Motor Manufacturing UK (NMUK), BP International, Nokia, ANGOKA, and Womble Bond Dickinson (UK) LLP, has been awarded £4 million by the Centre for Connected and Autonomous Vehicles, which was awarded through a Innovate UK competition, and matched by industry to a total £8 million'

The HGVs will operate without any personnel on board but will be monitored by a remote safety driver as backup.

Study co-author, Professor Phil Blythe CBE, Professor of Intelligent Transport Systems, and Head of the Future Mobility Group, Newcastle University, added: "We are delighted to be a part of the V-CAL project who have developed a first of its kind driverless tractor unit for logistics operations. At this stage it is expected that there will be a remote driver overseeing the operation of a number of driverless freight vehicles.

"What this research does is it begin to quantify the performance of remote divers and what this means for both the safety of driverless vehicles that will rely on such oversight and also the opportunity to reduce costs -- underpinned by real-world data and observations. What is happening in the NE with the funding of various Innovate UK projects in automation, really does put the region on the map as one of the significant areas of innovation in VAV and CAM."

Vantec Europe Managing Director, Martin Kendall, said: "Vantec were involved in the initial proof of concept trails for autonomous logistics which has led to V-CAL. We believe passionately that these early stage innovative transport solutions will be a means to supporting the HGV business not just in the UK but globally.

"This is one of the first studies to assess remote drivers' behaviour when teleoperating automated vehicles. The research findings offer practical resource for developing training programs, advancing technology, and refining operational protocols for the remote driving of automated vehicles."

This is one of the first studies to assess remote drivers' behaviour when teleoperating automated vehicles. The research findings offer practical resource for developing training programs, advancing technology, and refining operational protocols for the remote driving of automated vehicles.