The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention
Open accessAbstract
Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-tointerpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers etc., can understand what triggered a particular behavior. Here we explore the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network’s output (steering control). Our approach is two-stage. In the first stage, we use a visual attention model to train a convolution network endto- end from images to steering angle. The attention model highlights image regions that potentially influence the network’s output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network’s behavior. We demonstrate the effectiveness of our model on three datasets totaling 16 hours of driving. We first show that training with attention does not degrade the performance of the end-to-end network. Then we show that the network causally cues on a variety of features that are used by humans while driving.
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Cites
Deep Residual Learning for Image Recognition
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Autonomous driving in urban environments: Boss and the Urban Challenge
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Generating Visual Explanations
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Cited by
Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks
Chattopadhay, A., Sarkar, A., Howlader, P., & Balasubramanian, V. N. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00097 · Cited by 3,639
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167
“In deep learning, specific methods such as attention (41) and modular network architectures (42) provide limited insight into a network’s inner workings.”
BackgroundDeep learning-based image recognition for autonomous driving
Fujiyoshi, H., Hirakawa, T., & Yamashita, T. (2019) · IATSS Research · doi:10.1016/j.iatssr.2019.11.008 · Cited by 439
Explanations in Autonomous Driving: A Survey
Omeiza, D., Webb, H., Jirotka, M., & Kunze, L. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3122865 · Cited by 355
Atakishiyev, S., Salameh, M., Yao, H., & Goebel, R. (2021) · arXiv · doi:10.48550/arxiv.2112.11561 · Cited by 336
Metadata
- Authors
- Kim, J., & Canny, J.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 2961-2969
- OpenAlex
- W2963016445
- Cited by
- 366
- References
- 8
- FWCI
- 14.48
- Type
- conference paper
- Language
- en
- Open access
- green
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