A deep learning platooning-based video information-sharing Internet of Things framework for autonomous driving systems

Zishuo Zhou,Zahid Akhtar,Ka Lok Man,Kamran Siddique

doi:10.1177/1550147719883133

Abstract

To enhance the safety and stability of autonomous vehicles, we present a deep learning platooning-based video information-sharing Internet of Things framework in this study. The proposed Internet of Things framework incorporates concepts and mechanisms from several domains of computer science, such as computer vision, artificial intelligence, sensor technology, and communication technology. The information captured by camera, such as road edges, traffic lights, and zebra lines, is highlighted using computer vision. The semantics of highlighted information is recognized by artificial intelligence. Sensors provide information on the direction and distance of obstacles, as well as their speed and moving direction. The communication technology is applied to share the information among the vehicles. Since vehicles have high probability to encounter accidents in congested locations, the proposed system enables vehicles to perform self-positioning with other vehicles in a certain range to reinforce their safety and stability. The empirical evaluation shows the viability and efficacy of the proposed system in such situations. Moreover, the collision time is decreased considerably compared with that when using traditional systems.

Highlights

Autonomous control technologies gradually entered the vehicle market, including adaptive cruise control, automated emergency braking, and lane-changing and lane-keeping system for locking onto a path, resulting in full autonomy of a self-driving car
As a matter of fact, autonomous driving began in the 1980s when Navlab vehicles, which functioned in structured environments, were presented by Carnegie
In order to simulate driving at residential districts, urban roads, and highways, we considered 20, 40, 60, and 80 km/h speed wherever appropriate: 1. We release three to five autonomous vehicles by disabling the status of platooning-based information-sharing function on a simulating road at 20, 40, 60, and 80 km/h speed

Summary

Introduction

Autonomous control technologies gradually entered the vehicle market, including adaptive cruise control (acceleration/deceleration), automated emergency braking, and lane-changing and lane-keeping system for locking onto a path, resulting in full autonomy of a self-driving car. As a matter of fact, autonomous driving began in the 1980s when Navlab vehicles, which functioned in structured environments, were presented by Carnegie.

Objectives

Methods

Findings

Conclusion