Advancements and Insights in Assessing Cognitive Load during Driving: A Comprehensive Narrative Review

In the context of rapid advancements in the automotive industry and intelligent transportation systems, assessing cognitive load during driving has become a key factor for driving safety and user experience. This paper presents a comprehensive narrative review of theories, methods, and technological advancements in assessing cognitive load during driving. Rather than following a systematic review protocol,the structure of the review is organized around key research questions and critical gaps identified in the current literature. We assess the applicability and performance of major evaluation methods, including physiological indicators, behavioral measures, subjective self-report scales, and data-driven approaches, across various driving scenarios. The review also discusses the integration of multi-source information and propose a conceptual framework for holistic and adaptive cognitive load assessment. Furthermore, it highlights challenges in current practices, such as technical constraints, environmental variability, and individual differences. Special emphasis is placed on the relevance of cognitive load assessment for industrial informatics, particularly in the context of advanced driver assistance systems (ADAS), autonomous driving technologies, and driver training programs. This review aims to provide a structured synthesis of current approaches, offer practical insights for application and system design, and guide future research toward developing more robust, generalizable, and context-aware assessment tools. By analyzing the state of the art, we contribute a timely reference to support both academic development and industrial implementation for next-generation intelligent vehicle systems.