# StylePredict: Machine Theory of Mind for Human Driver Behavior From Trajectories

arXiv: https://arxiv.org/abs/2011.04816 · HTML: https://mlsanity.com/paper/2011.04816/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 11 of 11)

- [MMToM-QA: Multimodal Theory of Mind Question Answering](https://mlsanity.com/paper/2401.08743/) (2024)
- [Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning](https://mlsanity.com/paper/2312.05230/) (2023)
- [AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling](https://mlsanity.com/paper/2502.15676/) (2025)
- [Deep Interpretable Models of Theory of Mind](https://mlsanity.com/paper/2104.02938/) (2021)
- [METEOR:A Dense, Heterogeneous, and Unstructured Traffic Dataset With Rare Behaviors](https://mlsanity.com/paper/2109.07648/) (2021)
- [Voluntary Collusion with Secret Tools in Competing LLM Agents](https://mlsanity.com/paper/2605.27593/) (2026)
- [Robot Learning Theory of Mind through Self-Observation: Exploiting the Intentions-Beliefs Synergy](https://mlsanity.com/paper/2210.09435/) (2022)
- [Modeling Human Behavior Part I -- Learning and Belief Approaches](https://mlsanity.com/paper/2205.06485/) (2022)
- [Quantifying and Modeling Driving Styles in Trajectory Forecasting](https://mlsanity.com/paper/2503.04994/) (2025)
- [A formal definition and meta-model for a machine theory of mind](https://mlsanity.com/paper/2606.03471/) (2026)
- [Learning Latent Traits for Simulated Cooperative Driving Tasks](https://mlsanity.com/paper/2207.09619/) (2022)

## Abstract (arXiv)

Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic environment composed of human drivers and do not adapt to local conditions and socio-cultural norms. It is known that socially aware AVs can be designed if there exist a mechanism to understand the behaviors of human drivers. We present a notion of Machine Theory of Mind (M-ToM) to infer the behaviors of human drivers by observing the trajectory of their vehicles. Our M-ToM approach, called StylePredict, is based on trajectory analysis of vehicles, which has been investigated in robotics and computer vision. StylePredict mimics human ToM to infer driver behaviors, or styles, using a computational mapping between the extracted trajectory of a vehicle in traffic and the driver behaviors using graph-theoretic techniques, including spectral analysis and centrality functions. We use StylePredict to analyze driver behavior in different cultures in the USA, China, India, and Singapore, based on traffic density, heterogeneity, and conformity to traffic rules and observe an inverse correlation between longitudinal (overspeeding) and lateral (overtaking, lane-changes) driving styles.