Reference : Visualizing the Learning Progress of Self-Driving Cars
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/37215
Visualizing the Learning Progress of Self-Driving Cars
English
Mund, Sandro mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > > SEDAN]
Frank, Raphaël mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Varisteas, Georgios mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
State, Radu mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
2-Nov-2018
21st International Conference on Intelligent Transportation Systems
IEEE
2358-2363
Yes
978-1-7281-0322-8
21st International Conference on Intelligent Transportation Systems
from 02-11-2018 to 07-11-2018
IEEE
Maui
USA
[en] Convolutional Neural Networks ; Visualization ; Self-Driving Cars
[en] Using Deep Learning to predict lateral and longitudinal vehicle control, i.e. steering, acceleration and braking, is becoming increasingly popular. However, it remains widely unknown why those models perform so well. In order for them to become a commercially viable solution, it first needs to be understood why a certain behavior is triggered and how and what those networks learn from human-generated driving data to ensure safety. One research direction is to visualize what the network sees by highlighting regions of an image that influence the outcome of the model. In this vein, we propose a generic visualization method using Attention Heatmaps (AHs) to highlight what a given Convolutional Neural Network (CNN) learns over time. To do so, we rely on a novel occlusion technique to mask different regions of an input image to observe the effect on a predicted steering signal. We then gradually increase the amount of training data and study the effect on the resulting Attention Heatmaps, both in terms of visual focus and temporal behavior.
http://hdl.handle.net/10993/37215

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