# Beyond Empirical Windowing: An Attention-Based Approach for Trust Prediction in Autonomous Vehicles

arXiv: https://arxiv.org/abs/2312.10209 · HTML: https://mlsanity.com/paper/2312.10209/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [A Classification Model for Sensing Human Trust in Machines Using EEG and GSR](https://mlsanity.com/paper/1803.09861/) (2018)
- [Real-time Trust Prediction in Conditionally Automated Driving Using Physiological Measures](https://mlsanity.com/paper/2212.00607/) (2022)
- [Identification of Adaptive Driving Style Preference through Implicit Inputs in SAE L2 Vehicles](https://mlsanity.com/paper/2209.10536/) (2022)
- [Trust in Shared Automated Vehicles: Study on Two Mobility Platforms](https://mlsanity.com/paper/2303.09711/) (2023)

## Built on by (most cited first, 2 of 2)

- [Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives](https://mlsanity.com/paper/2402.02968/) (2024)
- [Self-Supervised Learning-Based Multimodal Prediction on Prosocial Behavior Intentions](https://mlsanity.com/paper/2507.08238/) (2025)

## Abstract (arXiv)

Humans' internal states play a key role in human-machine interaction, leading to the rise of human state estimation as a prominent field. Compared to swift state changes such as surprise and irritation, modeling gradual states like trust and satisfaction are further challenged by label sparsity: long time-series signals are usually associated with a single label, making it difficult to identify the critical span of state shifts. Windowing has been one widely-used technique to enable localized analysis of long time-series data. However, the performance of downstream models can be sensitive to the window size, and determining the optimal window size demands domain expertise and extensive search. To address this challenge, we propose a Selective Windowing Attention Network (SWAN), which employs window prompts and masked attention transformation to enable the selection of attended intervals with flexible lengths. We evaluate SWAN on the task of trust prediction on a new multimodal driving simulation dataset. Experiments show that SWAN significantly outperforms an existing empirical window selection baseline and neural network baselines including CNN-LSTM and Transformer. Furthermore, it shows robustness across a wide span of windowing ranges, compared to the traditional windowing approach.