End-to-end Deep Models for Self-driving Car


Date
Event
Intelligent Agent Group Seminar
Location
Room 210, Building 12, Hangzhou Dianzi University
Links

Reference:

  • Huval, Brody, et al. “An empirical evaluation of deep learning on highway driving.“ arXiv preprint arXiv:1504.01716 (2015).
  • Ulbrich, Simon, et al. “Towards a Functional System Architecture for Automated Vehicles.“ arXiv preprint arXiv:1703.08557 (2017).
  • Pomerleau, Dean A. “Alvinn: An autonomous land vehicle in a neural network.“  Advances in neural information processing systems. 1989.
  • Muller, Urs, et al. “Off-road obstacle avoidance through end-to-end learning.“ Advances in neural information processing systems. 2006.APA
  • Chen, Chenyi, et al. “Deepdriving: Learning affordance for direct perception in autonomous driving.“ Proceedings of the IEEE International Conference on Computer Vision. 2015.
  • Bojarski, Mariusz, et al. “End to end learning for self-driving cars.“ arXiv preprint arXiv:1604.07316 (2016).
  • Codevilla, Felipe, et al. “End-to-end driving via conditional imitation learning.“ arXiv preprint arXiv:1710.02410 (2017).
  • Xu, Huazhe, et al. “End-to-end learning of driving models from large-scale video datasets.“ arXiv preprint arXiv:1612.01079(2016).
  • Mukadam, Mustafa, et al. “Tactical Decision Making for Lane Changing with Deep Reinforcement Learning.” (2017).
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