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Visual attention for behavioral cloning in autonomous driving
Author(s): Tharun Mohandoss; Sourav Pal; Pabitra Mitra
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Paper Abstract

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.

Paper Details

Date Published: 15 March 2019
PDF: 11 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110411E (15 March 2019); doi: 10.1117/12.2522915
Show Author Affiliations
Tharun Mohandoss, IIT Kharagpur (India)
Sourav Pal, IIT Kharagpur (India)
Pabitra Mitra, IIT Kharagpur (India)

Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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