Reducing DNN labelling cost using surprise adequacy: an industrial case study for autonomous driving

  title={Reducing DNN labelling cost using surprise adequacy: an industrial case study for autonomous driving},
  author={Jinhan Kim and Jeongil Ju and Robert Feldt and Shin Yoo},
  journal={Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
  • Jinhan KimJeongil Ju S. Yoo
  • Published 29 May 2020
  • Computer Science
  • Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Deep Neural Networks (DNNs) are rapidly being adopted by the automotive industry, due to their impressive performance in tasks that are essential for autonomous driving. Object segmentation is one such task: its aim is to precisely locate boundaries of objects and classify the identified objects, helping autonomous cars to recognise the road environment and the traffic situation. Not only is this task safety critical, but developing a DNN based object segmentation module presents a set ofโ€ฆย 

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