Edit StudentProjectForm: Safe deployment of Deep Neural Networks in automotive engineering

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Summary:	MANDATORY: (provide a one-line* summary of the project)*
Programme:	OPTIONAL: (Name and the number of credits)
Keywords:	OPTIONAL: (Give 4-5 keywords)
TimeFrame:	MANDATORY: (Indicate time for the project, for example, Fall 2024)
References:	[1] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” CoRR, vol. abs/1512.03385, 2015. [Online]. Available: http://arxiv.org/abs/1512.03385 [2] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” CoRR, vol. abs/1409.4842, 2014. [Online]. Available: http://arxiv.org/abs/1409.4842 [3] A. H. Abdelaziz, S. Watanabe, J. R. Hershey, E. Vincent, and D. Kolossa, “Uncertainty propagation through deep neural networks,” in Interspeech 2015, Dresden, Germany, Sep. 2015. [Online]. Available: https://hal.inria.fr/hal-01162550 OPTIONAL: (Give 2-3 references for students to start with)
Prerequisites:	AI and learning system course OPTIONAL: (Courses, any other important information)
Supervisor(s):	MANDATORY: (can be multiple names, coma-separated)
Examiner:	OPTIONAL: (Name of project Examiner)
Author(s):	OPTIONAL: (can be multiple names, coma-separated)
Level:	MANDATORY: choose one, preferably <Master>
Status:	MANDATORY: choose <Open> when the topic is ready for students to select

Project description (free text)

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Research question
Whether it’s more software or hardware related
A very brief description of 3-4 work packages
Deliverables/outcomes/results

The overall goal of this project is to create a framework for deploying deep neural networks (DNN) in safety critical systems. Indeed DNN are popular and efficient, used with great success to solve pattern recognition tasks in various environments [1, 2]. However, to deploy such technology in safety critical systems, such as vehicles, deeper understanding on how and when they work as expected is needed. Deeper understanding makes it possible to detect weaknesses and take accurate precautions for improved reliability and safety. Robustness of the results produced by DNN is important for safety critical systems. This project will investigate the robustness of the results from a DNN by adding noise to the input data set, removing some data points or inserting inaccurate data and calculating the propagation of the uncertainty through the network [3]. The thesis will focus on evaluation of DNN factors w.r.t. robustness. Possible data that can be used for the project is either Deeptraffic (http://selfdrivingcars.mit.edu/deeptraffic/) or Torcs (https://sourceforge.net/projects/torcs/).

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Edit StudentProjectForm: Safe deployment of Deep Neural Networks in automotive engineering

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