Networking and Communications in Autonomous Driving: A Survey

By Jiadai Wang, Jiajia Liu, Nei Kato. IEEE Communications Surveys and Tutorials, 2019

450 citations recorded by the source corpus.

The development of light detection and ranging, Radar, camera, and other advanced sensor technologies inaugurated a new era in autonomous driving. However, due to the intrinsic limitations of these sensors, autonomous vehicles are prone to making erroneous decisions and causing serious disasters. At this point, networking and communication technologies can greatly make up for sensor deficiencies, and are more reliable, feasible and efficient to promote the information interaction, thereby improving autonomous vehicle’s perception and planning capabilities as well as realizing better vehicle control. This paper surveys the networking and communication technologies in autonomous driving from two aspects: intra- and inter-vehicle. The intra-vehicle network as the basis of realizing autonomous driving connects the on-board electronic parts. The inter-vehicle network is the medium for interaction between vehicles and outside information. In addition, we present the new trends of communication technologies in autonomous driving, as well as investigate the current mainstream verification methods and emphasize the challenges and open issues of networking and communications in autonomous driving.

Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/de6445d98c8735b4716dc70f73be020754a91d3a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Nei Kato
2. Fengxiao Tang
3. Tie Qiu
4. Yuichi Kawamoto
5. Zhaolong Ning
6. Jiajia Liu
7. Mohammed Atiquzzaman

Citations in the corpus

• Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges (2020, background): “…suddenly encounters pedestrians or other vehicles scrambling for the lane while driving, OBU obtains environmental information by lidar and HD cameras, quickly analyzes the semantics of environmental information, and sends the instruction of immediate deceleration to ensure driving safety [47].”
• Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020, background): “Recently, the new generation vehicle-related works, such as autonomous driving, cooperative vehicular networks, Internet of Vehicles (IoV) [2], vehicular ad hoc networks (VANETs), air-to-ground (A2G) networks, and space–air– ground integrated networks (SAGINs) [3], have attracted considerable attent”