scholarly journals Reinforcement Learning and Deep Learning Based Lateral Control for Autonomous Driving [Application Notes]

2019 ◽  
Vol 14 (2) ◽  
pp. 83-98 ◽  
Author(s):  
Dong Li ◽  
Dongbin Zhao ◽  
Qichao Zhang ◽  
Yaran Chen
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Autonomous Driving ◽  
Lateral Control

scholarly journals Edge-Sensitive Left Ventricle Segmentation Using Deep Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2375
Author(s):  
Jingjing Xiong ◽  
Lai-Man Po ◽  
Kwok Wai Cheung ◽  
Pengfei Xian ◽  
Yuzhi Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Left Ventricle ◽  
Autonomous Driving ◽  
Learning Methods ◽  
Proposed Model ◽  
Markov Decision ◽  
Edge Points ◽  
Cardiac Diagnosis ◽  
Ventricle Segmentation

Deep reinforcement learning (DRL) has been utilized in numerous computer vision tasks, such as object detection, autonomous driving, etc. However, relatively few DRL methods have been proposed in the area of image segmentation, particularly in left ventricle segmentation. Reinforcement learning-based methods in earlier works often rely on learning proper thresholds to perform segmentation, and the segmentation results are inaccurate due to the sensitivity of the threshold. To tackle this problem, a novel DRL agent is designed to imitate the human process to perform LV segmentation. For this purpose, we formulate the segmentation problem as a Markov decision process and innovatively optimize it through DRL. The proposed DRL agent consists of two neural networks, i.e., First-P-Net and Next-P-Net. The First-P-Net locates the initial edge point, and the Next-P-Net locates the remaining edge points successively and ultimately obtains a closed segmentation result. The experimental results show that the proposed model has outperformed the previous reinforcement learning methods and achieved comparable performances compared with deep learning baselines on two widely used LV endocardium segmentation datasets, namely Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset, and Sunnybrook 2009 dataset. Moreover, the proposed model achieves higher F-measure accuracy compared with deep learning methods when training with a very limited number of samples.

Author response for "Deep learning and reinforcement learning approach on microgrid"

2020 ◽  
Author(s):  
Kumar Chandrasekaran ◽  
Prabaakaran Kandasamy ◽  
Srividhya Ramanathan
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Author Response ◽  
Learning Approach

Enabling Rewards for Reinforcement Learning in Laser Beam Welding processes through Deep Learning

2020 ◽  
Author(s):  
Markus Schmitz ◽  
Florian Pinsker ◽  
Alexander Ruhri ◽  
Beibei Jiang ◽  
Georgij Safronov
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Laser Beam ◽  
Laser Beam Welding ◽  
Welding Processes

scholarly journals Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses

2021 ◽  
pp. 1-1
Author(s):  
Yao Deng ◽  
Tiehua Zhang ◽  
Guannan Lou ◽  
Xi Zheng ◽  
Jiong Jin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autonomous Driving ◽  
Attacks And Defenses

Deep Learning-Based Ground Vibration Monitoring: Reinforcement Learning and RNN-CNN Approach

2021 ◽  
pp. 1-5
Author(s):  
Sangseok Yun ◽  
Jae-Mo Kang ◽  
Jeongseok Ha ◽  
Sangho Lee ◽  
Dong-Woo Ryu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Ground Vibration ◽  
Vibration Monitoring

Transfer Reinforcement Learning for Autonomous Driving

2021 ◽  
Vol 31 (3) ◽  
pp. 1-26
Author(s):  
Aravind Balakrishnan ◽  
Jaeyoung Lee ◽  
Ashish Gaurav ◽  
Krzysztof Czarnecki ◽  
Sean Sedwards
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Transfer Problem ◽  
Autonomous Driving ◽  
High Fidelity ◽  
Rule Based ◽  
High Level ◽  
Real Vehicle

Reinforcement learning (RL) is an attractive way to implement high-level decision-making policies for autonomous driving, but learning directly from a real vehicle or a high-fidelity simulator is variously infeasible. We therefore consider the problem of transfer reinforcement learning and study how a policy learned in a simple environment using WiseMove can be transferred to our high-fidelity simulator, W ise M ove . WiseMove is a framework to study safety and other aspects of RL for autonomous driving. W ise M ove accurately reproduces the dynamics and software stack of our real vehicle. We find that the accurately modelled perception errors in W ise M ove contribute the most to the transfer problem. These errors, when even naively modelled in WiseMove , provide an RL policy that performs better in W ise M ove than a hand-crafted rule-based policy. Applying domain randomization to the environment in WiseMove yields an even better policy. The final RL policy reduces the failures due to perception errors from 10% to 2.75%. We also observe that the RL policy has significantly less reliance on velocity compared to the rule-based policy, having learned that its measurement is unreliable.

scholarly journals Deep Inverse Reinforcement Learning for Behavior Prediction in Autonomous Driving: Accurate Forecasts of Vehicle Motion

2021 ◽  
Vol 38 (1) ◽  
pp. 87-96
Author(s):  
Tharindu Fernando ◽  
Simon Denman ◽  
Sridha Sridharan ◽  
Clinton Fookes
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Driving ◽  
Behavior Prediction ◽  
Inverse Reinforcement Learning ◽  
Vehicle Motion

scholarly journals Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

2020 ◽  
pp. 1-21
Author(s):  
Khan Muhammad ◽  
Amin Ullah ◽  
Jaime Lloret ◽  
Javier Del Ser ◽  
Victor Hugo C. de Albuquerque
Keyword(s):  
Deep Learning ◽  
Autonomous Driving ◽  
Future Directions ◽  
Driving Current
Sign in / Sign up

Export Citation Format

Share Document