2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-AVV.9
Paper Title LEARNING UNCERTAINTY FOR SAFETY-ORIENTED SEMANTIC SEGMENTATION IN AUTONOMOUS DRIVING
Authors Victor Besnier, valeo, France; David Picard, ENPC, France; Alexandre Briot, valeo, France
SessionSS-AVV: Special Session: Autonomous Vehicle Vision
LocationArea A
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Special Sessions: Autonomous Vehicle Vision
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this paper, we show how uncertainty estimation can be leveraged to enable safety critical image segmentation in autonomous driving, by triggering a fallback behavior if a target accuracy cannot be guaranteed. We introduce a new uncertainty measure based on disagreeing predictions as measured by a dissimilarity function. We propose to estimate this dissimilarity by training a deep neural architecture in parallel to the task-specific network. It allows this observer to be dedicated to the uncertainty estimation, and let the task-specific network make predictions. We propose to use self-supervision to train the observer, which implies that our methods does not require additional training data. We show experimentally that our proposed approach is much less computationally intensive at inference time than competing methods MCDropout, while delivering better results on safety-oriented evaluation metrics on the CamVid dataset, especially in the case of glare artifacts.