Markov Chain Analysis of Gaze Transitions: Assessing Attention Management in SAE Level 3 Automated Driving

This work-in-progress examines how the use of different non-driving related task (NDRT) interfaces (mobile phone vs. head-up display and baseline) influences drivers’ visual attention and scanning strategies during conditional automated driving. We present preliminary findings from a driving simulator study (N=46) that utilised Markov Chain analysis of gaze transitions and gaze dispersion metrics. Results show that NDRTs, particularly on mobile phones, compromise drivers’ attention distribution and gaze transitions. While HUDs keep drivers’ gaze closer to the road, they may still hinder hazard perception. Markov Chain analysis reveals valuable insights into drivers’ attention management, informing the design of safer in-vehicle interfaces for automated vehicles. These findings highlight the need for careful consideration when designing in-vehicle interfaces and NDRT interactions for automated vehicles.