scholarly journals Agile Autonomous Driving using End-to-End Deep Imitation Learning

2018 ◽  
Author(s):  
Yunpeng Pan ◽  
Ching-An Cheng ◽  
Kamil Saigol ◽  
Keuntaek Lee ◽  
Xinyan Yan ◽  
...  
Keyword(s):  
Autonomous Driving ◽  
Imitation Learning ◽  
End To End

scholarly journals Self-Driving Vehicles Using End to End Deep Imitation Learning

2021 ◽  
Vol 9 (5) ◽  
pp. 33-43
Author(s):  
Ashraf Nabil ◽  
Ayman Kassem
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Autonomous Driving ◽  
Imitation Learning ◽  
Automotive Applications ◽  
Strong Artificial Intelligence ◽  
End To End ◽  
Difficult Situations

Autonomous Driving is one of the difficult problems faced the automotive applications. Nowadays, it is restricted due to the presence of some laws that prevent cars from being fully autonomous for the fear of accidents occurrence. Researchers try to improve the accuracy and safety of their models with the aim of having a strong push against these restricted Laws. Autonomous driving is a sought-after solution which isn’t easily solved by classical approaches. Deep Learning is considered as a strong Artificial Intelligence paradigm which can teach machines how to behave in difficult situations. It proved its success in many differ domains, but it still has sometime in the automotive applications. The presented work will use the end-to-end deep machine learning field in order to reach to our goal of having Full Autonomous Driving Vehicle that can behave correctly in different scenarios. CARLA simulator will be used to learn and test the deep neural networks. Results will show not only performance on CARLA’s simulator as an end-to-end solution for autonomous driving, but also how the same approach can be used on one of the most popular real datasets of automotive that includes camera images with the corresponding driver’s control action.

End-to-End Deep Conditional Imitation Learning for Autonomous Driving

2019 ◽  
Author(s):  
Mohammed Abdou ◽  
Hanan Kamal ◽  
Samah El-Tantawy ◽  
Ali Abdelkhalek ◽  
Omar Adel ◽  
...  
Keyword(s):  
Autonomous Driving ◽  
Imitation Learning ◽  
End To End

scholarly journals PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving

2020 ◽  
Author(s):  
Kuan-Hui Lee ◽  
Matthew Kliemann ◽  
Adrien Gaidon ◽  
Jie Li ◽  
Chao Fang ◽  
...  
Keyword(s):  
Autonomous Driving ◽  
Flow Estimation ◽  
End To End

scholarly journals End-to-End Autonomous Driving Through Dueling Double Deep Q-Network

2021 ◽  
Author(s):  
Baiyu Peng ◽  
Qi Sun ◽  
Shengbo Eben Li ◽  
Dongsuk Kum ◽  
Yuming Yin ◽  
...  
Keyword(s):  
Neural Network ◽  
State Space ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Autonomous Driving ◽  
Saliency Map ◽  
Hierarchical Architecture ◽  
Link Type ◽  
The Neural Network ◽  
End To End

AbstractRecent years have seen the rapid development of autonomous driving systems, which are typically designed in a hierarchical architecture or an end-to-end architecture. The hierarchical architecture is always complicated and hard to design, while the end-to-end architecture is more promising due to its simple structure. This paper puts forward an end-to-end autonomous driving method through a deep reinforcement learning algorithm Dueling Double Deep Q-Network, making it possible for the vehicle to learn end-to-end driving by itself. This paper firstly proposes an architecture for the end-to-end lane-keeping task. Unlike the traditional image-only state space, the presented state space is composed of both camera images and vehicle motion information. Then corresponding dueling neural network structure is introduced, which reduces the variance and improves sampling efficiency. Thirdly, the proposed method is applied to The Open Racing Car Simulator (TORCS) to demonstrate its great performance, where it surpasses human drivers. Finally, the saliency map of the neural network is visualized, which indicates the trained network drives by observing the lane lines. A video for the presented work is available online, https://youtu.be/76ciJmIHMD8 or https://v.youku.com/v_show/id_XNDM4ODc0MTM4NA==.html.

scholarly journals iCurb: Imitation Learning-Based Detection of Road Curbs Using Aerial Images for Autonomous Driving

2021 ◽  
Vol 6 (2) ◽  
pp. 1097-1104
Author(s):  
Zhenhua Xu ◽  
Yuxiang Sun ◽  
Ming Liu
Keyword(s):  
Autonomous Driving ◽  
Aerial Images ◽  
Imitation Learning

Imitation Learning for Autonomous Driving Based on Convolutional and Recurrent Neural Networks

2021 ◽  
Author(s):  
Chunling Du ◽  
Zhenbiao Wang ◽  
Andrew Alexander Malcolm ◽  
Choon Lim Ho
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Autonomous Driving ◽  
Imitation Learning

scholarly journals Visually Guided Picking Control of an Omnidirectional Mobile Manipulator Based on End-to-End Multi-Task Imitation Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 1882-1891
Author(s):  
Chi-Yi Tsai ◽  
Yung-Shan Chou ◽  
Ching-Chang Wong ◽  
Yu-Cheng Lai ◽  
Chien-Che Huang
Keyword(s):  
Imitation Learning ◽  
Mobile Manipulator ◽  
Visually Guided ◽  
End To End
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