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CS298 ProposalCrowd-sourcing Data for Autonomous Driving and Applying ItYaoyan Xi (Yaoyan.Xi@sjsu.edu) Advisor: Dr. Chris Pollett (Chris@Pollett.org) Committee Members: Dr. Robert Chun (Robert.Chun@sjsu.edu) Dr. Mingkun Li (Mingkun_Li@apple.com) Abstract:This project strives to build an Android App that is capable of collecting the road scenery and car driving data and uses such data to train a neural network model to provide the correct instructions during autonomous driving. It is a fact that every road has already been driven on by someone at some point and will likely be driven on by someone again in the future. It is also a fact that mobile phones are ubiquitous, so it seems promising to collect car driving data using a mobile application and then use that data to train autonomous vehicle AI systems. We will in this project develop applications to actually do this. We will use the Convolutional Neural Network (CNN) model and the training will be done through Python sklearn library. CS297 ResultsI have developed and tested three separate Android Apps to detect a user's real time GPS location, to capture a vehicle's movement to correlate with steering wheel's movement direction and degrees, and to record the traffic scenery and save to an external storage. All serves as the preparation for the coming integrated autonomous driving App. Proposed Schedule
Key Deliverables:
Innovations and Challenges
References:[Long2007] A Review of Intelligent Systems Software for Autonomous Vehicles. Long,L., Hanford,S., Janrathitikarn, O., Sinsley, G., Miller, J. Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Security and Defense Applications. 2007. [Menze2015] Object Scene Flow for Autonomous Vehicles. Menze,M.,Geiger,A. Computer Vision Foundation. 2015. [Pomerleau1991] Efficient Training of Artificial Neural Networks for Autonomous. Navigation. Pomerleau, D.A. In Neural Computation 3:1 pp. 88-97. 1991. |