Emerging Information and Communication Technologies for Autonomous Driving
摘要截稿:
全文截稿: 2018-11-01
影响因子: 11.391
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 计算机科学 - 1区
• 小类 : 计算机:硬件 - 1区
• 小类 : 计算机:信息系统 - 1区
• 小类 : 工程:电子与电气 - 1区
• 小类 : 电信学 - 1区
Overview
With the great advances in Information and Communication Technologies (ICTs), autonomous driving is anticipated to significantly improve driving safety by performing intelligent operations such as collision avoidance, lane departure warning, traffic sign detection, etc., which alleviates the burden of human drivers, especially under some hostile traveling environments. In addition, by steering deceleration and acceleration of an autonomous vehicle, autonomous driving can boost fuel economy and lower emissions. With the support of autonomous driving, new transportation services for elderly people and persons with handicaps tends to be fully developed. Autonomous driving has been an emerging area to achieve the ultimate in automobile safety and comfort, which will greatly change the way we work, live, and play.
The developments of the multidisciplinary autonomous driving require state-of-the-art technologies in perception, planning, and control. Even though an autonomous vehicle is typically equipped with a powerful computing processor and various kinds of sensing devices (e.g., camera, sensor, radar, etc.), an inherent drawback of a single ICT technology in the existing autonomous driving tends to result in suboptimal performance. That enforces us to seek an integration of various ICT technologies. For example, the coordination among vehicles by vehicle-to-everything (e.g., vehicle, infrastructure, road, human, sensor) communications (V2X) can overcome the limited range of sensors and achieve cooperative maneuvering and sensing, which enables moving vehicles to quickly and accurately collect real-time road traffic information and notify neighboring vehicles of potential dangerous events. It meets the imminent demands towards reduced traffic accidents and improved road efficiency in Intelligent Transportation System (ITS). In addition, a deep learning-based approach can be utilized to detect unexpected obstacles on the road ahead, reinforcement learning can be applied to motion control and decision-making for autonomous vehicles. Integrating various ICT technologies can optimize decision-making of intelligent vehicles, and improve safety, efficiency, sustainability of transportation systems. In order to explore autonomous driving combined with the cross-domain knowledge in wireless communications, mobile computing, artificial intelligence, data processing, etc., this special issue will focus on the following subjects of interest, as well as related issues:
Smart sensors and sensing technologies for autonomous driving
Localization and navigation technologies for autonomous driving
Wireless communication technologies for intelligent and connected vehicles
Resource (e.g., spectrum resources, energy sources) management in vehicular communications
5G wireless transmission technologies for intelligent and connected vehicles
Intra/Inter-vehicle communications for autonomous driving
Communication and networking architectures (e.g., 5G, SDN) for autonomous driving
Fog/Edge computing for autonomous driving
Vehicular cloud computing for autonomous driving
Big data analysis/mining for autonomous driving
Transportation data processing for autonomous driving
Deep/Reinforcement learning for autonomous driving
QoS and QoE of systems, applications, and services for autonomous driving
Testbed, implementation and deployment for autonomous driving