<p>Physiological deep learning methods.</p>

This review was not registered. This review aims to (1) identify driver-state constructs that have been investigated in conditional automated driving, examining how they are defined and operationalized; (2) characterize sensing modalities used for driver-state monitoring; (3) evaluate computational approaches including multimodal fusion used in previous studies; (4) examine performance metrics, validation strategies and real-time feasibility; (5) identify methodological limitations, reporting inconsistencies and priorities for future development and deployment.

This study suggests the standardisation of Level 3 datasets. It also calls for harmonised operational definitions and more representative sampling. It finally advises the performance of more longitudinal on-road validation.

This work was supported by the National Natural Science Foundation of China.</p></div>