# Emotion in an active inference model of human driving

arXiv: https://arxiv.org/abs/2608.07480 · HTML: https://mlsanity.com/paper/2608.07480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 13 of 13)

- [Wayformer: Motion Forecasting via Simple & Efficient Attention Networks](https://mlsanity.com/paper/2207.05844/) (2022)
- [The Waymo Open Sim Agents Challenge](https://mlsanity.com/paper/2305.12032/) (2023)
- [FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs](https://mlsanity.com/paper/2211.16197/) (2022)
- [Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World](https://mlsanity.com/paper/2003.07739/) (2020)
- [Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving](https://mlsanity.com/paper/2001.04509/) (2020)
- [Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference](https://mlsanity.com/paper/2311.06417/) (2023)
- [Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study](https://mlsanity.com/paper/2305.15187/) (2023)
- [TrajFlow: Learning Distributions over Trajectories for Human Behavior Prediction](https://mlsanity.com/paper/2304.05166/) (2023)
- [Free Energy in a Circumplex Model of Emotion](https://mlsanity.com/paper/2407.02474/) (2024)
- [Active inference as a unified model of collision avoidance behavior in human drivers](https://mlsanity.com/paper/2506.02215/) (2025)
- [Validation of human benchmark models for Automated Driving System approval: How competent and careful are they really?](https://mlsanity.com/paper/2406.09493/) (2024)
- [Looking for an out: Affordances, uncertainty and collision avoidance behavior of human drivers](https://mlsanity.com/paper/2505.14842/) (2025)
- [Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model](https://mlsanity.com/paper/2604.19838/) (2026)

## Abstract (arXiv)

Active inference has emerged as a principled framework for modeling adaptive behavior by balancing goal-directed action with uncertainty reduction. It has been successfully applied across biological and artificial systems, including recent work on human driving. However, existing active inference models of driving have yet to address an important determinant of behavior in traffic: affective state, which significantly influences decision-making. Prior work in non-traffic domains has explored active inference agents in which emotions are represented along the axes of valence and arousal in the circumplex model. However, this work has been limited to simplified settings with discrete state spaces. In this work, we propose an expanded formulation of valence and arousal that can be extracted from a more complex active inference model of driving with continuous states. In particular, we condition affective estimates not only on the current state but also on predicted future outcomes. We evaluate the proposed approach in two interactive driving scenarios and show that the resulting emotion signals correspond to affective patterns reported in similar scenarios.