Attention sharing detection represents a major advancement in Guardian’s distraction detection, measuring not just where a driver looks, but precisely how much time they spend with their eyes off the road.

With drivers more connected than ever, the risk of distraction behind the wheel is a central concern for fleet operators, insurers, and regulatory bodies. Even a glance of just two seconds away from the road can double the risk of a crash¹. As in-cab distractions, such as mobile phone use, continue to rise, the industry is looking for new ways to measure and manage distraction risk more precisely.

Guardian’s attention sharing detection capability directly addresses this demand.

So, what exactly is “attention sharing”? In simple terms, attention sharing refers to when a driver repeatedly looks away from the road for brief moments within a short span of time. Take, for instance, glancing down at a mobile phone and then back up to the road – each glance may last only a split second, but when repeated, these moments quickly accumulate. As the driver’s eyes shift away from the road, situational awareness erodes, and their ability to detect and respond to hazards becomes impaired – raising the risk of a crash.

Research shows that as drivers divide their visual attention, their awareness of the driving environment becomes compromised². This degradation in situational awareness is linked to poorer lateral vehicle control and delayed hazard detection³. Critically, accumulated eyes-off-road time – a hallmark of attention sharing behaviour – is strongly associated with increased crash risk^4,5. Even when glances are brief, frequent shifts in gaze can impair a driver’s ability to respond to emerging hazards. Recent findings by Mulhall et al. (2024)⁶ further underscore the risk: phone use is more likely to occur during attention sharing behaviour than during long glances away.

Guardian’s attention sharing detection catches these subtleties and changes the game. Continually tracking a driver’s eye gaze direction, rather than just head orientation, provides a far more accurate picture of where the driver’s attention is focused. This means distraction is detected more reliably, with fewer false alarms and more true positives. The result? Better safety insights and less unnecessary annoyance for drivers.

Instead of focusing on discrete, binary events, attention sharing captures attention patterns over time. This makes Guardian’s approach more predictive. By tracking visual engagement as a continuous metric, the system can identify risky behaviour trends before they lead to incidents. Whenever the cumulative time spent looking off-road exceeds 10 seconds within any 30-second period, the system springs into action. Instantly, a real-time audio alert sounds, accompanied by a distraction icon on Guardian’s LED screen – an unmistakable cue for the driver to refocus their attention.

In a time when mobile phone usage continues to rise globally – despite policies, laws and public awareness campaigns around the risks it poses – the ability to detect and intervene in real time when drivers are distracted is more important than ever.

Guardian’s seamless vigilance weaves safety into every journey, ensuring that even fleeting lapses do not slip through unnoticed.

Regulations are also evolving, with the EU’s General Safety Regulation mandating Advanced Driver Distraction Warning (ADDW) systems in new vehicles. Meanwhile, EuroNCAP continues to raise the bar with stricter safety rating criteria. Guardian’s attention sharing detection helps fleets stay ahead of both safety risks and compliance requirements.

In conclusion, short off-road glances may seem insignificant on their own, but the data tells a different story. When these brief distractions accumulate, they can be just as deadly as a long glance away.

With attention sharing detection, Guardian empowers fleets to manage driver distraction in a way that’s never been possible before. In today’s complex driving environment, that capability isn’t just helpful – it’s essential.

References

¹ Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J. D., & Ramsey, D. J. (2006). The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data (No. DOT HS 810 594). United States. Department of Transportation. National Highway Traffic Safety Administration.

² Tivesten, E., & Dozza, M. (2014). Driving context and visual-manual phone tasks influence glance behavior in naturalistic driving. Transportation research part F: traffic psychology and behaviour, 26, 258-272.

³ Horberry, T., Anderson, J., Regan, M. A., Triggs, T. J., & Brown, J. (2006). Driver distraction: The effects of concurrent in-vehicle tasks, road environment complexity and age on driving performance. Accident Analysis & Prevention, 38(1), 185-191.

⁴ Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J. D., & Ramsey, D. J. (2006). The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data (No. DOT HS 810 594). United States. Department of Transportation. National Highway Traffic Safety Administration.

⁵ Olson, R. L., Hanowski, R. J., Hickman, J. S., & Bocanegra, J. (2009). Driver distraction in commercial vehicle operations (No. FMCSA-RRT-09-042). United States. Department of Transportation. Federal Motor Carrier Safety Administration.

⁶ Mulhall, M., Wilson, K., Yang, S., Kuo, J., Sletten, T., Anderson, C., … & Lenné, M. G. (2024). European NCAP driver state monitoring protocols: Prevalence of distraction in naturalistic driving. Human factors, 66(9), 2205-2217.