# Measuring Surprise in the Wild

arXiv: https://arxiv.org/abs/2305.07733 · HTML: https://mlsanity.com/paper/2305.07733/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 76 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 7 of 7)

- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [Wayformer: Motion Forecasting via Simple & Efficient Attention Networks](https://mlsanity.com/paper/2207.05844/) (2022)
- [Scaling active inference](https://mlsanity.com/paper/1911.10601/) (2019)
- [A taxonomy of surprise definitions](https://mlsanity.com/paper/2209.01034/) (2022)
- [Human Inference in Changing Environments With Temporal Structure](https://mlsanity.com/paper/2101.11143/) (2021)
- [Collision Avoidance Testing of the Waymo Automated Driving System](https://mlsanity.com/paper/2212.08148/) (2022)
- [Learning An Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following](https://mlsanity.com/paper/2303.15201/) (2023)

## Built on by (most cited first, 4 of 4)

- [Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference](https://mlsanity.com/paper/2311.06417/) (2023)
- [Surprise Potential as a Measure of Interactivity in Driving Scenarios](https://mlsanity.com/paper/2502.05677/) (2025)
- [Mutual Information Surprise: Rethinking Unexpectedness in Autonomous Systems](https://mlsanity.com/paper/2508.17403/) (2025)
- [Towards Defining, Measuring, and Leveraging Creativity in AI Applications](https://mlsanity.com/paper/2610.12845/) (2026)

## Abstract (arXiv)

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.