Active Vision in Driving: Joint Modeling of Scanpaths and Risk Perception.

Under the Active Vision hypothesis, eye movements are not passive responses to visual stimuli but are actively guided by task demands and internal goals. In driving, scanpaths may therefore reflect an ongoing process of information sampling for risk assessment. However, current computational models often isolate scanpath prediction from risk assessment, overlooking their intrinsic cognitive coupling. In this study, we investigate whether driver scanpaths and traffic risk perception can be jointly modeled within a unified framework. We propose a computational approach based on the introduced Adversarial Inverse Reinforcement Learning (AIRL), where gaze behavior is interpreted as a policy that maximizes a latent safety-related reward. By employing a generator to simulate human-like sequences of fixations and saccades, and a discriminator to approximate the internal reward signal, our framework ensures that generated scanpaths synergistically inform downstream risk perception. To facilitate this research, we constructed the BDDA dataset, aggregating over 13,000 spatio-temporal gaze points with explicit risk annotations to study this joint mechanism. Experimental results indicate that simultaneously modeling the "where" (scanpath dynamics) and the "why" (risk perception) significantly outperforms the compared baseline methods on the proposed BDDA dataset. These findings provide computational evidence for a functional coupling between visual attention and risk perception, supporting the view that eye movements serve as an active mechanism for acquiring task-relevant information in safety-critical environments.