Modelling attention allocation and takeover performance in two-stage takeover system via a cognitive computational...

Studies have demonstrated two-stage takeover systems' feasibility and advantages. However, existing cognitive models mainly focus on simulating drivers' performance in single-stage takeover systems, with limited insights into cognitive modelling of effects of monitoring requests (MRs) within two-stage takeover systems. This study constructed a cognitive computational model for two-stage takeover systems based on queueing network-adaptive control of thought rational (QN-ACTR) architecture. Our model aims to capture variations in drivers' attention allocation and takeover performance resulting from different MR experiences. Five components, representing distinct cognitive processes, were designed to closely align with drivers' behavioural patterns. This model was validated through an experiment using metrics such as percentage time in road-centre and takeover time. Results revealed significant concordance between the model predictions and experimental data, with R-squared ≥ 0.76, RMSE ≤ 0.41, and MAPE ≤ 15%. The findings of this work extended beyond the two-stage takeover system investigation to include human factor modelling.