Visual Attention for Behavioral Cloning in Autonomous Driving
This work addresses the problem of enhancing autonomous driving performance for safer and more efficient vehicles, but it appears incremental as it builds on existing behavioral cloning techniques.
The paper tackles improving autonomous driving by using visual attention, presenting supervised and unsupervised methods to predict attention maps, and finds that the supervised approach performs better than other methods.
The goal of our work is to use visual attention to enhance autonomous driving performance. We present two methods of predicting visual attention maps. The first method is a supervised learning approach in which we collect eye-gaze data for the task of driving and use this to train a model for predicting the attention map. The second method is a novel unsupervised approach where we train a model to learn to predict attention as it learns to drive a car. Finally, we present a comparative study of our results and show that the supervised approach for predicting attention when incorporated performs better than other approaches.