Driver mental workload measurement trends and cognitive impacts: a literature review

Driver mental workload (DWL) is a critical determinant of driving safety and performance, particularly amid increasing modern vehicle automation and human–machine interaction. This literature review synthesises 59 peer-reviewed studies published between 2020 and 2025, building on earlier foundational reviews to examine recent advancements in DWL assessment methods, neuroergonomic and embodied perspectives on workload, and associated cognitive impacts. DWL measurement approaches are grouped into four domains: subjective reports, physiological indicators, behavioural metrics, and hybrid machine learning models. Key findings highlight growing emphasis on real-time monitoring, multimodal data integration, neurophysiologically informed and affective workload indicators, and adaptive interface design. High DWL impairs hazard detection and lane control, while underload, particularly in automated scenarios, reduces situational awareness. Challenges remain in standardising assessment protocols and applying scalable, interpretable technologies, and the review is constrained by its focus on English-language, Scopus-indexed journal articles from 2020 to 2025. The review identifies research gaps and advocates for ethically grounded, personalised DWL detection systems that integrate cognitive, emotional, and contextual factors. It aims to support the design of human-centered transportation systems that enhance safety, responsiveness, and trust in automated environments and to clarify how workload measurement advances inform ergonomics theory in human–automation collaboration.