AADS: Augmented autonomous driving simulation using data-driven algorithms-Reference-Cited by-同舟云学术

AADS: Augmented autonomous driving simulation using data-driven algorithms

Published:2019-03-13 Issue:28 Volume:4 Page:
ISSN:2470-9476
Container-title:Science Robotics
language:en
Short-container-title:Sci. Robot.

Author:

Li W.¹²³^ORCID,Pan C. W.⁴⁵^ORCID,Zhang R.⁶^ORCID,Ren J. P.⁶^ORCID,Ma Y. X.⁷^ORCID,Fang J.¹²^ORCID,Yan F. L.¹²^ORCID,Geng Q. C.⁸^ORCID,Huang X. Y.¹²^ORCID,Gong H. J.³,Xu W. W.⁶,Wang G. P.⁴^ORCID,Manocha D.⁹^ORCID,Yang R. G.¹²^ORCID

Affiliation:

1. Baidu Research, Beijing, China.

2. National Engineering Laboratory of Deep Learning Technology and Application, Beijing, China.

3. Nanjing University of Aeronautics and Astronautics, Nanjing, China.

4. Beijing Engineering Technology Research Center of Virtual Simulation and Visualization, Peking University, Beijing, China.

5. Deepwise AI Lab, Beijing, China.

6. Zhejiang University, Hangzhou, China.

7. University of Hong Kong, Hong Kong, China.

8. Beihang University, Beijing, China.

9. University of Maryland, College Park, MD, USA.

Abstract

A scalable simulation system combines augmented images with synthesized traffic to enable training and testing of autonomous driving.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Mechanical Engineering

Reference48 articles.

1. Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?;Kalra N.;Transp. Res. A Policy Pract.,2016

2. A. Gaidon Q. Wang Y. Cabon E. Vig Virtual worlds as proxy for multi-object tracking analysis in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016).

3. Sim4CV: A photo-realistic simulator for computer vision applications;Müller M.;Int. J. Comput. Vis.,2018

4. A. Dosovitskiy G. Ros F. Codevilla A. Lopez V. Koltun CARLA: An open urban driving simulator in Proceedings of the 1st Annual Conference on Robot Learning (PMLR 2017) pp. 1–16.

5. AirSim: High-fidelity visual and physical simulation for autonomous vehicles;Shah S.;Field Serv. Rob.,2018

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