# Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study

arXiv: https://arxiv.org/abs/2305.15187 · HTML: https://mlsanity.com/paper/2305.15187/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 5 of 5)

- [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021)
- [The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence](https://mlsanity.com/paper/2002.06177/) (2020)
- [ForceFormer: Exploring Social Force and Transformer for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2302.07583/) (2023)
- [Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios](https://mlsanity.com/paper/2211.05455/) (2022)
- [The COMMOTIONS Urban Interactions Driving Simulator Study Dataset](https://mlsanity.com/paper/2305.11909/) (2023)

## Built on by (most cited first, 5 of 5)

- [ARMCHAIR: integrated inverse reinforcement learning and model predictive control for human-robot collaboration](https://mlsanity.com/paper/2402.19128/) (2024)
- [STEP: Structured Training and Evaluation Platform for benchmarking trajectory prediction models](https://mlsanity.com/paper/2509.14801/) (2025)
- [Smooth-Trajectron++: Augmenting the Trajectron++ behaviour prediction model with smooth attention](https://mlsanity.com/paper/2305.19678/) (2023)
- [General-purpose LLMs as Models of Human Driver Behavior: The Case of Simplified Merging](https://mlsanity.com/paper/2604.09609/) (2026)
- [Emotion in an active inference model of human driving](https://mlsanity.com/paper/2608.07480/) (2026)

## Abstract (arXiv)

The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style. Reliable models predicting human behavior are essential for overcoming this issue. While data-driven models are commonly used to this end, they can be vulnerable in safety-critical edge cases. This has led to an interest in models incorporating cognitive theory, but as such models are commonly developed for explanatory purposes, this approach's effectiveness in behavior prediction has remained largely untested so far. In this article, we investigate the usefulness of the Commotions model -- a novel cognitively plausible model incorporating the latest theories of human perception, decision-making, and motor control -- for predicting human behavior in gap acceptance scenarios, which entail many important traffic interactions such as lane changes and intersections. We show that this model can compete with or even outperform well-established data-driven prediction models across several naturalistic datasets. These results demonstrate the promise of incorporating cognitive theory in behavior prediction models for automated vehicles.