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Enhanced Behavioral Cloning Based self-driving Car Using Transfer Learning [article]

Uppala Sumanth, Narinder Singh Punn, Sanjay Kumar Sonbhadra, Sonali Agarwal
2020 arXiv   pre-print
In recent years, several deep learning-based behavioral cloning approaches have been developed in the context of self-driving cars specifically based on the concept of transfer learning.  ...  Behavioral cloning is the process of replicating human behavior via visuomotor policies by means of machine learning algorithms.  ...  ., are trying to bring millions of self-driving or autonomous cars on the road by utilizing deep learning approaches.  ... 
arXiv:2007.05740v1 fatcat:vuy35ejz5fdehbanikg4gdbdli

When Do Drivers Concentrate? Attention-based Driver Behavior Modeling With Deep Reinforcement Learning [article]

Xingbo Fu, Feng Gao, Jiang Wu
2020 arXiv   pre-print
the driver' s attention allocation for consecutive time steps in car-following model.  ...  Considering reaction time, we construct the attention mechanism in the actor network to capture temporal dependencies of consecutive observations.  ...  This fact has attracted attention of several researchers attempting to capture driver behaviors in modeling car-following behavior.  ... 
arXiv:2002.11385v2 fatcat:p6sfnrnpzzdsbdbodxrhkvbc7i

Integration of Cloud Computing and Artificial Intelligence in Driverless Car Using High Performance Behavioural Cloning

Prabhat Kumar, Vishal Shrivastava
2019 International Journal on Recent and Innovation Trends in Computing and Communication  
Deep Learning has demonstrated its extraordinary computational potential by transcending its abilities into more complex areas, where pattern matching, image recognition and behavioral cloning plays a  ...  The system consists of Image Processing and analyzing of the training data into behavioral cloning of the vehicle in a simulated environment.  ...  That means a dataset is generated in the simulator by user driven car in training mode, and the deep neural network model then drives the car in autonomous mode.  ... 
doi:10.17762/ijritcc.v7i8.5347 fatcat:g26m3vwki5dvnfvmmidsl5x3ka

Deep Learning Based Driver Smoking Behavior Detection for Driving Safety

Tzu-Chih Chien, Department of Electrical Engineering, National Chung Hsing University, Taiwan, Chieh-Chuan Lin, Chih-Peng Fan
2020 Journal of Image and Graphics  
behavior detection, deep learning, YOLOv2, driving safety  ...  By the YOLOv2 deep learning network, the preprepared images of driver smoking behavior are marked, and the cigarette detector is trained to detect the cigarette object when the driver is smoking.  ...  Figure 4 . 4 The cigarette detection by YOLO-based deep learning method III.  ... 
doi:10.18178/joig.8.1.15-20 fatcat:vvoawgzzbrgw7nvxyt2obgfh4u

Self Driving Car using Deep Learning Technique

Chirag Sharma, VELLORE INSTITUTE OF TECHNOLOGY
2020 International Journal of Engineering Research and  
neural networks and deep learning techniques.  ...  The neural network trains the deep learning technique on the basis of photos taken from a camera in manual mode which provides a condition for running the car in autonomous mode, utilizing the trained  ...  BEHAVIORAL CLONING [1] Behavioral cloning is a technique by which subpsychological aptitudes like -perceiving objects, understanding while simultaneously performing an action can be captured and imitated  ... 
doi:10.17577/ijertv9is060247 fatcat:yldgsmhsizhdfcwad65q54mgsm

Self-driving scale car trained by Deep reinforcement learning [article]

Qi Zhang, Tao Du, Changzheng Tian
2019 arXiv   pre-print
To improve the generalization ability for the driving behavior, the reinforcement learning method requires extrinsic reward from the real environment, which may damage the car.  ...  A theoretical model is established and analyzed in the virtual simulation environment, and it is trained by double Deep Q-network. Then, the trained model is migrated to a scale car in real world.  ...  RELATED WORK Our aim is making a self-driving car trained by deep reinforcement learning.  ... 
arXiv:1909.03467v3 fatcat:ouahvtbf2bhzrpq7c5iqfc246y

Learning the Car-following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning

Yang Zhou, Rui Fu, Chang Wang, Lelitha Vanajakshi
2020 Journal of Advanced Transportation  
The present study proposes a framework for learning the car-following behavior of drivers based on maximum entropy deep inverse reinforcement learning.  ...  Moreover, the proposed model captured the characteristics of different driving styles during car-following scenarios.  ...  Car-following is one of the most common situations encountered by drivers. e modeling of car-following behavior has been a common research focus in the fields of traffic simulation [4] , advanced driver-assistance  ... 
doi:10.1155/2020/4752651 fatcat:n4iek5lchjcrxf2wzbu55oigxi

Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection [article]

Abenezer Girma, Seifemichael Amsalu, Abrham Workineh, Mubbashar Khan, Abdollah Homaifar
2020 arXiv   pre-print
Our approach uses a deep bidirectional Long Short-Term Memory (LSTM) with an attention mechanism model based on a hybrid-state system (HSS) framework.  ...  Over the last few years and with the recent advances in the field of deep learning, researchers have utilized variant of these methods to solve driver behavior modeling tasks [13] , [14] .  ...  As shown in [10] - [12] , [22] these observation can capture and describe drivers behavior at road intersection .  ... 
arXiv:2006.05918v1 fatcat:ymuni7trirhazpqpahdvn44q3m

Traffic Forecasting using Vehicle-to-Vehicle Communication [article]

Steven Wong, Lejun Jiang, Robin Walters, Tamás G. Molnár, Gábor Orosz, Rose Yu
2021 arXiv   pre-print
In order to best utilize real-world V2V communication data, we integrate first principle models with deep learning.  ...  We conduct a comprehensive study to evaluate different methods of integrating first principle models with deep learning techniques.  ...  Acknowledgments This research was partially supported by the University of Michigan's Center of Connected and Automated Transportation through the US DOT grant 69A3551747105, Google Faculty Research Award  ... 
arXiv:2104.05528v1 fatcat:hby6aw26ubh7jn7dedtgbp72wm

Deep Reinforcement Learning framework for Autonomous Driving

AhmadEL Sallab, Mohammed Abdou, Etienne Perot, Senthil Yogamani
2017 IS&T International Symposium on Electronic Imaging Science and Technology  
Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework for autonomous driving using deep reinforcement learning.  ...  As it is a relatively new area of research for autonomous driving, we provide a short overview of deep reinforcement learning and then describe our proposed framework.  ...  The rest of the paper is organized as follows; first we discuss the related work, then the Meta learning framework is described followed by the MetaDAgger algorithm.  ... 
doi:10.2352/issn.2470-1173.2017.19.avm-023 fatcat:oarg7sq2pvay3a25i3ee43qina

Motion Prediction on Self-driving Cars: A Review [article]

Shahrokh Paravarzar, Belqes Mohammad
2020 arXiv   pre-print
As a result, deep reinforcement learning is the best candidate to tackle self-driving cars.  ...  The state-of-the-art consists of classical and physical methods, deep learning networks, and reinforcement learning. prons and cons of the methods and gap of the research presented in this review.  ...  Deep learning approach for Autonomous Vehicles The deep learning methods for AV behavior prediction can be categories into three classes (i) recurrent neural network, (ii) convolutional neural network,  ... 
arXiv:2011.03635v1 fatcat:3452qcxglbeo5elkidc6pdb43u

Gaze Training by Modulated Dropout Improves Imitation Learning [article]

Yuying Chen, Congcong Liu, Lei Tai, Ming Liu, Bertram E. Shi
2019 arXiv   pre-print
Imitation learning by behavioral cloning is a prevalent method that has achieved some success in vision-based autonomous driving.  ...  Typically, a convolutional neural network learns to predict the steering commands from raw driver-view images by mimicking the behaviors of human drivers.  ...  INTRODUCTION End-to-end deep learning has captured much attention and has been widely applied in many autonomous control systems.  ... 
arXiv:1904.08377v2 fatcat:yleqwqx2nfbjbab5ela26p67ne

Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments [article]

Maxime Bouton, Alireza Nakhaei, Kikuo Fujimura, Mykel J. Kochenderfer
2019 arXiv   pre-print
To make the decision strategy robust to perception errors and occlusions, we introduce a belief update technique using a learning based approach.  ...  We empirically demonstrate that our algorithm outperforms rule-based methods and reinforcement learning techniques on a complex intersection scenario.  ...  Tram et al. propose a deep reinforcement learning approach with recurrent neural networks to learn how to navigate intersections with multiple vehicles with changing behaviors [7] .  ... 
arXiv:1904.11483v1 fatcat:niiykwcshfbbpeahvfflqmttpy

Self Driving RC Car using Behavioral Cloning [article]

Aliasgar Haji, Priyam Shah, Srinivas Bijoor
2019 arXiv   pre-print
We have used the concept of behavioral cloning to convert a normal RC model car into an autonomous car using Deep Learning technology  ...  The first truly autonomous cars appeared in the 1980s with projects funded by DARPA( Defense Advance Research Project Agency ).  ...  [2] Behavioral cloning is a method by which sub-cognitive skills like -recognizing objects, experience while performing an action can be captured and reproduced in a computer program.  ... 
arXiv:1910.06734v1 fatcat:ghf2xiruwbc3dcsujdaxr546mu

Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving [article]

Meixin Zhu, Yinhai Wang, Ziyuan Pu, Jingyun Hu, Xuesong Wang, Ruimin Ke
2019 arXiv   pre-print
A model used for velocity control during car following was proposed based on deep reinforcement learning (RL).  ...  Car-following behavior produced by the model were compared with that observed in the empirical NGSIM data, to demonstrate the model's ability to follow a lead vehicle safely, efficiently, and comfortably  ...  ACKNOWLEDGEMENTS This study was sponsored by the Chinese National Science Foundation (51522810).  ... 
arXiv:1902.00089v2 fatcat:4xryj3ekordidm36bev4kn6upm
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