Measuring Surprise in the Wild
This work addresses the challenge of extending surprise measurement from labs to real-world dynamic environments, with applications in traffic safety and potential generalization to other domains.
The authors tackled the problem of measuring surprise in naturalistic settings by combining computational models of surprise with machine-learned generative models, demonstrating their approach in road traffic to detect surprising human behavior and showing advantages over existing measures.
The quantitative measurement of how and when we experience surprise has mostly remained limited to laboratory studies, and its extension to naturalistic settings has been challenging. Here we demonstrate, for the first time, how computational models of surprise rooted in cognitive science and neuroscience combined with state-of-the-art machine learned generative models can be used to detect surprising human behavior in complex, dynamic environments like road traffic. In traffic safety, such models can support the identification of traffic conflicts, modeling of road user response time, and driving behavior evaluation for both human and autonomous drivers. We also present novel approaches to quantify surprise and use naturalistic driving scenarios to demonstrate a number of advantages over existing surprise measures from the literature. Modeling surprising behavior using learned generative models is a novel concept that can be generalized beyond traffic safety to any dynamic real-world environment.