How task demands influence driver behaviour in conditionally automated driving: An investigation of situation awareness and takeover performance

In conditionally automated driving, driver capability and task demands are crucial for safe takeover transitions. Identifying factors influencing task demand and driver capability, as well as exploring their combined effects on driver behaviour and perception, are essential for developing models that optimise driver performance. To simulate varying task demands, we adjusted the urgency of the takeover time budget (TOTB) and the complexity of traffic scenarios (i.e., TOR-Lane, the lane where the vehicle was located when the takeover request occurred), while manipulating driver capability by introducing non-driving related tasks (NDRTs). A multilevel modelling approach was employed to analyse how these factors jointly influenced takeover behaviour and situation awareness (SA). Results indicated that TOTB, NDRT, and TOR-Lane influenced takeover timeliness at different time stages: NDRT affected driver reaction time, while TOTB and TOR-Lane impacted information processing time (IPT). A shorter TOTB resulted in reduced IPT and lower minimum time-to-collision [min (TTC)], especially when visual-cognitive NDRT were involved, which further impaired takeover quality. Moreover, increased traffic environment complexity prolonged IPT and reduced min (TTC). To meet task demands, drivers adjusted their visual behaviour to rapidly restore SA by reducing the quality of visual processing for low-priority elements, thereby prioritising resources to takeover tasks. Participants’ SA improved as TOTB increased, reaching saturation levels that varied with scenario complexity—7–9 s in the centre lane and 5–7 s in the side lane. This study reveals how driver behavioural patterns are influenced by task demands and their own capabilities, supporting the design of adaptable human–machine interaction models.