Unsupervised Real-to-Virtual Domain Unification for End-to-End Highway Driving

By Luona Yang, Xiaodan Liang, Eric P. Xing. 2018

20 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xiaodan Liang
2. Liang Lin
3. Bochao Wang
4. Eric P. Xing
5. Mengshi Zhang
6. Tairui Wang
7. Yuqun Zhang
8. Yimin Chen

Citations in the corpus

• A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, critical): “…where the model fails to generate a virtual car at the position at which there is a real car in the real image (This kind of performance is as reported by Yang et al. (2018) whithout constraining the semantic consistency), which could potentially lead to accidents in self-driving scenarios.”
• CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving (2018, background): “Recently, more efforts have been devoted to the second type, that is, end-to-end approaches that learn to map sensory input to control commands [9,8,1,18,11,19].”
• DeepRoad: GAN-Based Metamorphic Testing and Input Validation Framework for Autonomous Driving Systems (2018, methodology): “Moreover, GAN-based domain adaption is also applied for virtualto-real and real-to-virtual driving scene adaption [26, 42].”
• Toward Characteristic-Preserving Image-based Virtual Try-On Network (2018, background): “image from one domain to another domain [12, 38, 5, 34, 18, 19, 35].”