Modelling attention allocation and takeover performance in two-stage takeover system via a cognitive computational model: considering the role of multiple monitoring requests
该研究构建了一个基于QN-ACTR架构的认知计算模型，用于模拟两阶段接管系统中驾驶员因不同监控请求经历而产生的注意力分配和接管性能变化，并通过实验验证了模型的有效性。
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.