Beyond Empirical Windowing: An Attention-Based Approach for Trust Prediction in Autonomous Vehicles
This addresses the problem of trust prediction in human-machine interaction for autonomous vehicles, offering a robust method that reduces reliance on empirical windowing, though it is incremental in improving existing techniques.
The paper tackles the challenge of modeling gradual human states like trust in autonomous vehicles, where label sparsity complicates identifying critical state shifts in long time-series data, and proposes SWAN, which outperforms baselines including CNN-LSTM and Transformer on a new multimodal driving dataset.
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.