scispace - formally typeset
Open AccessJournal ArticleDOI

Artificial Intelligence for Vehicle-to-Everything: A Survey

Reads0
Chats0
TLDR
This paper presents a comprehensive survey of the research works that have utilized AI to address various research challenges in V2X systems and summarized the contribution and categorized them according to the application domains.
Abstract
Recently, the advancement in communications, intelligent transportation systems, and computational systems has opened up new opportunities for intelligent traffic safety, comfort, and efficiency solutions. Artificial intelligence (AI) has been widely used to optimize traditional data-driven approaches in different areas of the scientific research. Vehicle-to-everything (V2X) system together with AI can acquire the information from diverse sources, can expand the driver's perception, and can predict to avoid potential accidents, thus enhancing the comfort, safety, and efficiency of the driving. This paper presents a comprehensive survey of the research works that have utilized AI to address various research challenges in V2X systems. We have summarized the contribution of these research works and categorized them according to the application domains. Finally, we present open problems and research challenges that need to be addressed for realizing the full potential of AI to advance V2X systems.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

TL;DR: This survey extensively summarizes existing works in this field by categorizing them with respect to application types, control models and studied algorithms and discusses the challenges and open questions regarding deep RL-based transportation applications.
Journal ArticleDOI

Study on artificial intelligence: The state of the art and future prospects

TL;DR: This study presents an overview of the scope of artificial intelligence using background, drivers, technologies, and applications, as well as logical opinions regarding the development of artificial Intelligence.
Journal ArticleDOI

A Survey on Machine-Learning Techniques for UAV-Based Communications

TL;DR: This article provides a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.
Journal ArticleDOI

Technology development of electric vehicles: A review

TL;DR: In this article, the authors provide a comprehensive review of the technical development of EVs and emerging technologies for their future application, including batteries, charging technology, electric motors and control, and charging infrastructure of EVs.
Journal ArticleDOI

AI for Next Generation Computing: Emerging Trends and Future Directions

TL;DR: In this article , the authors discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.
References
More filters
Journal ArticleDOI

Human-level control through deep reinforcement learning

TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Journal ArticleDOI

Deep learning in neural networks

TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Journal ArticleDOI

On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming

TL;DR: A comprehensive description of the primal-dual interior-point algorithm with a filter line-search method for nonlinear programming is provided, including the feasibility restoration phase for the filter method, second-order corrections, and inertia correction of the KKT matrix.
Journal ArticleDOI

Vision meets robotics: The KITTI dataset

TL;DR: A novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research, using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras and a high-precision GPS/IMU inertial navigation system.
Related Papers (5)