Journal ArticleDOI
Computer Architectures for Autonomous Driving
TLDR
To enable autonomous driving, a computing stack must simultaneously ensure high performance, consume minimal power, and have low thermal dissipation—all at an acceptable cost.Abstract:
To enable autonomous driving, a computing stack must simultaneously ensure high performance, consume minimal power, and have low thermal dissipation—all at an acceptable cost. An architecture that matches workload to computing units and implements task time-sharing can meet these requirements.read more
Citations
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Journal ArticleDOI
Edge Computing for Autonomous Driving: Opportunities and Challenges
TL;DR: In this paper, the authors review state-of-the-art approaches in these areas as well as explore potential solutions to address these challenges, including providing enough computing power, redundancy, and security so as to guarantee the safety of autonomous vehicles.
Journal ArticleDOI
Mobile Edge Intelligence and Computing for the Internet of Vehicles
Jun Zhang,Khaled Ben Letaief +1 more
TL;DR: Key design issues, methodologies, and hardware platforms are introduced, including edge-assisted perception, mapping, and localization for intelligent IoV, and typical use cases for intelligent vehicles are illustrated.
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.
Book
Creating Autonomous Vehicle Systems
TL;DR: This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience and the authors share their practical experiences of creating autonomous vehicle systems.
Proceedings ArticleDOI
OpenVDAP: An Open Vehicular Data Analytics Platform for CAVs
TL;DR: An Open Vehicular Data Analytics Platform (OpenVDAP) for CAVs is proposed, which is a full-stack edge based platform including an on-board computing/communication unit, an isolation-supported and security & privacy-preserved vehicle operation system, an edge-aware application library, as well as an optimal workload of?oading and scheduling strategy, allowing CAVs to dynamically detect each service's status, computation overhead and the optimal of?:oading destination.
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
A method for registration of 3-D shapes
Paul J. Besl,H.D. McKay +1 more
TL;DR: In this paper, the authors describe a general-purpose representation-independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point.
Book
Probabilistic Robotics
TL;DR: This research presents a novel approach to planning and navigation algorithms that exploit statistics gleaned from uncertain, imperfect real-world environments to guide robots toward their goals and around obstacles.
Journal ArticleDOI
Visual Odometry [Tutorial]
TL;DR: Visual odometry is the process of estimating the egomotion of an agent (e.g., vehicle, human, and robot) using only the input of a single or If multiple cameras attached to it, and application domains include robotics, wearable computing, augmented reality, and automotive.
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