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Quantifying the Remote Driver’s Interaction with 5G-Enabled Level 4 Automated Vehicles: A Real-World Study

Year: 2024

Type: article

Abstract: This real-world investigation aimed to quantify the human–machine interaction between remote drivers of teleoperation systems and the Level 4 automated vehicle in a real-world setting. The primary goal was to investigate the effects of disengagement and distraction on remote driver performance and behaviour. Key findings revealed that mental disengagement, achieved through distraction via a reading task, significantly slowed the remote driver’s reaction time by an average of 5.309 s when the Level 4 automated system required intervention. Similarly, disengagement resulted in a 4.232 s delay in decision-making time for remote drivers when they needed to step in and make critical strategic decisions. Moreover, mental disengagement affected the remote drivers’ attention focus on the road and increased their cognitive workload compared to constant monitoring. Furthermore, when actively controlling the vehicle remotely, drivers experienced a higher cognitive workload than in both “monitoring” and “disengagement” conditions. The findings emphasize the importance of designing teleoperation systems that keep remote drivers actively engaged with their environment, minimise distractions, and reduce disengagement. Such designs are essential for enhancing safety and effectiveness in remote driving scenarios, ultimately supporting the successful deployment of Level 4 automated vehicles in real-world applications.

Source: Electronics

DOI: 10.3390/electronics13224366

Authors: Shuo Li, Yanghanzi Zhang, Simon Edwards, Phil Blythe

Institution: Newcastle University

Language: English

Topic: Human-Automation Interaction and Safety

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,728