In everyday activities where continuous visual awareness is critical such as driving, several cognitive processes pertaining to visual attention are of the essence, for instance, change detection, anticipation, monitoring, etc. Research suggests that environmental load and task difficulty contribute to failures in visual perception that can be essential for detecting and reacting to safety-critical incidents. However, it is unclear how gaze patterns and attentional strategies are compromised because of environmental complexity in naturalistic driving. In a change detection task during everyday simulated driving, we investigate inattention blindness in relation to environmental complexity and the kind of interaction incidents drivers address. We systematically analyse and evaluate safety-critical situations from real-world driving videos and replicate a number of them in a virtual driving experience. Participants (N= 80) aged 23-45 years old, drove along three levels of environmental complexity (low-medium-high) and various incidents of interaction with roadside users (e.g., pedestrians, cyclists, pedestrians in a wheelchair), categorized as safety critical or not. Participants detected changes in the behaviour of road users and in object properties. We collect multimodal data including eye-tracking, egocentric view videos, movement trace, head movements, driving behaviour, and detection button presses. Results suggest that gaze behaviour (number and duration of fixations, 1st fixation on AOI) is affected negatively by an increase in environmental complexity, but the effect is moderate for safety-critical incidents. Moreover, anticipatory and monitoring attention was crucial for detecting critical changes in behaviour and reacting on time. However, in highly complex environments participants effectively limit attentional monitoring and lingering for non-critical changes and they also controlled “look-but-fail-to-see errors", especially while addressing a safety-related event. We conclude that drivers change attentional strategies, avoiding non-productive forms of attentional elaboration (anticipatory and monitoring) and efficiently disengaging from targets when the task difficulty is high. We discuss the implications for driving education and research driven development of autonomous driving.