Enhanced End-to-End System for Autonomous Driving Using Deep Convolutional Networks

2021 ◽  
pp. 81-101
Author(s):  
Balaji Muthazhagan ◽  
Suriya Sundaramoorthy
Keyword(s):  
Autonomous Driving ◽  
Convolutional Networks ◽  
End To End

scholarly journals End-to-End Deep Neural Network Architectures for Speed and Steering Wheel Angle Prediction in Autonomous Driving

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1266
Author(s):  
Pedro J. Navarro ◽  
Leanne Miller ◽  
Francisca Rosique ◽  
Carlos Fernández-Isla ◽  
Alberto Gila-Navarro
Keyword(s):  
Autonomous Vehicles ◽  
High Performance ◽  
Autonomous Driving ◽  
Optimization Process ◽  
Mixed Data ◽  
Steering Wheel ◽  
Driver Assistance Systems ◽  
Convolutional Networks ◽  
Perception System ◽  
End To End

The complex decision-making systems used for autonomous vehicles or advanced driver-assistance systems (ADAS) are being replaced by end-to-end (e2e) architectures based on deep-neural-networks (DNN). DNNs can learn complex driving actions from datasets containing thousands of images and data obtained from the vehicle perception system. This work presents the classification, design and implementation of six e2e architectures capable of generating the driving actions of speed and steering wheel angle directly on the vehicle control elements. The work details the design stages and optimization process of the convolutional networks to develop six e2e architectures. In the metric analysis the architectures have been tested with different data sources from the vehicle, such as images, XYZ accelerations and XYZ angular speeds. The best results were obtained with a mixed data e2e architecture that used front images from the vehicle and angular speeds to predict the speed and steering wheel angle with a mean error of 1.06%. An exhaustive optimization process of the convolutional blocks has demonstrated that it is possible to design lightweight e2e architectures with high performance more suitable for the final implementation in autonomous driving.

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 Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

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.

Affinity Graph Based End-to-End Deep Convolutional Networks for CT Hemorrhage Segmentation

2019 ◽  
pp. 546-555 ◽  
Author(s):  
Jungrae Cho ◽  
Inchul Choi ◽  
Jaeil Kim ◽  
Sungmoon Jeong ◽  
Young-Sup Lee ◽  
...  
Keyword(s):  
Convolutional Networks ◽  
End To End

scholarly journals End-to-End Blood Pressure Prediction via Fully Convolutional Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 185458-185468 ◽  
Author(s):  
Sanghyun Baek ◽  
Jiyong Jang ◽  
Sungroh Yoon
Keyword(s):  
Blood Pressure ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
End To End
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