Saliency-Based Attention Shifting: A Framework for Improving Driver Situational Awareness of Out-of-Label Hazards

摘要

The advent of autonomous driving systems promises to transform transportation by enhancing safety, efficiency, and comfort. As these technologies evolve toward higher levels of autonomy, the need for integrated systems that seamlessly support human involvement in decision-making becomes increasingly critical. Certain scenarios necessitate human involvement, including those where the vehicle is unable to identify an object or element in the scene, and as such cannot take independent action. Therefore, situational awareness is essential to mitigate potential risks during a takeover, where a driver must assume control and autonomy from the vehicle. The need for driver attention is important to avoid collisions with external agents and ensure a smooth transition during takeover operations. This paper explores the integration of attention redirection techniques, such as gaze manipulation through targeted visual and auditory cues, to help drivers maintain focus on emerging hazards and reduce target fixation in semi-autonomous driving scenarios. We propose a conceptual framework that combines real-time gaze tracking, context-aware saliency analysis, and synchronized visual and auditory alerts to enhance situational awareness, proactively address potential hazards, and foster effective collaboration between humans and autonomous systems.