Towards an Interpretable Deep Driving Network by Attentional Bottleneck | IEEE Journals & Magazine | IEEE Xplore

Towards an Interpretable Deep Driving Network by Attentional Bottleneck


Abstract:

Deep neural networks are a key component of behavior prediction and motion generation for self-driving cars. One of their main drawbacks is a lack of transparency: they s...Show More

Abstract:

Deep neural networks are a key component of behavior prediction and motion generation for self-driving cars. One of their main drawbacks is a lack of transparency: they should provide easy to interpret rationales for what triggers certain behaviors. We propose an architecture called Attentional Bottleneck with the goal of improving transparency. Our key idea is to combine visual attention, which identifies what aspects of the input the model is using, with an information bottleneck that enables the model to only use aspects of the input which are important. This not only provides sparse and interpretable attention maps (e.g. focusing only on specific vehicles in the scene), but it adds this transparency at no cost to model accuracy. In fact, we find improvements in accuracy when applying Attentional Bottleneck to the ChauffeurNet model, whereas we find that the accuracy deteriorates with a traditional visual attention model.
Published in: IEEE Robotics and Automation Letters ( Volume: 6, Issue: 4, October 2021)
Page(s): 7349 - 7356
Date of Publication: 13 July 2021

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