Journal ArticleDOI
Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning
TLDR
Simulated results demonstrate the desired driving behaviors of an autonomous vehicle using both the reinforcement learning and inverse reinforcement learning techniques.About:
This article is published in Robotics and Autonomous Systems.The article was published on 2019-04-01. It has received 172 citations till now. The article focuses on the topics: Reinforcement learning & Markov decision process.read more
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Digital Twin: Values, Challenges and Enablers From a Modeling Perspective
TL;DR: This work reviews the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.
Posted Content
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey
TL;DR: A taxonomy and categorizing the XAI techniques based on their scope of explanations, methodology behind the algorithms, and explanation level or usage which helps build trustworthy, interpretable, and self-explanatory deep learning models is proposed.
Journal ArticleDOI
Artificial intelligence applications in the development of autonomous vehicles: a survey
TL;DR: Insight is provided into potential opportunities regarding the use of AI in conjunction with other emerging technologies: 1) high definition maps, big data, and high performance computing; 2) augmented reality / virtual reality enhanced simulation platform; and 3) 5G communication for connected AVs.
Journal ArticleDOI
Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues
TL;DR: A comprehensive survey of deep learning applications for object detection and scene perception in autonomous vehicles examines the theory underlying self-driving vehicles from deep learning perspective and current implementations, followed by their critical evaluations.
Journal ArticleDOI
Complete coverage path planning using reinforcement learning for Tetromino based cleaning and maintenance robot
Anirudh Krishna Lakshmanan,Anirudh Krishna Lakshmanan,Rajesh Elara Mohan,Balakrishnan Ramalingam,Anh Vu Le,Prabahar Veerajagadeshwar,Kamlesh Tiwari,Muhammad Ilyas,Muhammad Ilyas +8 more
TL;DR: A complete coverage path planning model trained using deep blackreinforcement learning (RL) for the tetromino based reconfigurable robot platform called hTetro to simultaneously generate the optimal set of shapes for any pretrained arbitrary environment shape with a trajectory that has the least overall cost.
References
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Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
Journal ArticleDOI
Information Theory and Statistical Mechanics. II
TL;DR: In this article, the authors consider statistical mechanics as a form of statistical inference rather than as a physical theory, and show that the usual computational rules, starting with the determination of the partition function, are an immediate consequence of the maximum-entropy principle.
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