Driver inattention – particularly the “eyes-on mind-off” phenomenon – remains a persistent challenge for road safety. While visual distraction is well-addressed by current driver monitoring systems (DMS), cognitive distraction (CD) and mental overload are less understood and harder to detect. Yet both contribute to a spectrum of disengagement from driving that can result in critical failures, such as Looked But Failed To See (LBFTS) crashes.
This presentation reframes the conversation around CD, arguing that what is often labelled as cognitive distraction is more accurately described as inattention – whether due to external distraction or internal cognitive load. With Euro NCAP’s 2029 protocols set to include CD assessment, the industry must consider whether CD alone captures the full scope of the problem, or whether a broader inattention framework is needed.
Drawing on a decade of research in attention modelling, and our own research, we present a system-level approach to managing driver (in)attention. This includes real-time detection of ocular indicators of disengagement, and adaptive interventions informed by both internal cognitive state and external driving context. By recognising that high cognitive load can impair driving focus, we propose a threshold-based model of attentional resource depletion to guide intervention strategies. Ultimately, addressing “eyes-on mind-off” crashes requires smarter sensing, nuanced terminology, and a shift from distraction detection to inattention management.
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