Interpretable Self-Attention Temporal Reasoning for Driving Behavior Understanding

Performing driving behaviors based on causal reasoning is essential to ensure driving safety. In this work, we investigated how state-of-the-art 3D Convolutional Neural Networks (CNNs) perform on classifying driving behaviors based on causal reasoning. We proposed a perturbation-based visual explana...

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Bibliographic Details
Published in:arXiv.org 2019-11
Main Authors: Liu, Yi-Chieh, Yung-An Hsieh, Min-Hung, Chen, Chao-Han, Huck Yang, Tegner, Jesper, Yi-Chang, James Tsai
Format: Article
Language:English
Subjects:
Artificial neural networks
Behavior
Model accuracy
Perturbation
Reasoning
Salience
Temporal logic
Traffic signals
Vehicle safety
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Summary:Performing driving behaviors based on causal reasoning is essential to ensure driving safety. In this work, we investigated how state-of-the-art 3D Convolutional Neural Networks (CNNs) perform on classifying driving behaviors based on causal reasoning. We proposed a perturbation-based visual explanation method to inspect the models' performance visually. By examining the video attention saliency, we found that existing models could not precisely capture the causes (e.g., traffic light) of the specific action (e.g., stopping). Therefore, the Temporal Reasoning Block (TRB) was proposed and introduced to the models. With the TRB models, we achieved the accuracy of \(\mathbf{86.3\%}\), which outperform the state-of-the-art 3D CNNs from previous works. The attention saliency also demonstrated that TRB helped models focus on the causes more precisely. With both numerical and visual evaluations, we concluded that our proposed TRB models were able to provide accurate driving behavior prediction by learning the causal reasoning of the behaviors.
ISSN:2331-8422
DOI:10.48550/arxiv.1911.02172