مقاله انگلیسی رایگان در مورد امنیت وسایل نقلیه خودمختار – IEEE 2019

I. INTRODUCTION

II. PRELIMINARIES

III. COLLABORATIVE ANALYSIS FRAMEWORK

IV. IMPLEMENTATION OF COLLABORATIVE ANALYSIS FRAMEWORK

V. FRAMEWORK IMPLEMENTATION EXAMPLE

VI. CONCLUSION AND FUTURE WORK

REFERENCES

Human error has been statistically proven to be the primary cause of road accidents. This undoubtedly is a contributory cause of the rising popularity of autonomous vehicles as they are presumably able to maneuver appropriately/optimally on the roads while diminishing the likelihood of human error and its repercussion. However, autonomous vehicles are not ready for widespread adoption because their safety and security issues are yet to be thoroughly investigated/addressed. Little literature could be found on collaborative analysis of safety and security of autonomous vehicles. This paper proposes a framework for analyzing both safety and security issues, which includes an integrated safety and security method (S&S) with international vehicle safety and security standards ISO 26262 and SAE J3061. The applicability of the proposed framework is demonstrated using an example of typical autonomous vehicle model. Using this framework, one can clearly understand the vehicle functions, structure, the associated failures and attacks, and also see the vulnerabilities that are not yet addressed by countermeasures, which helps to improve the in-vehicle safety and security from researching and engineering perspectives.

INTRODUCTION

An ever increasing number of vehicles on the roads worldwide has apparently increased the frequency of the traffic accidents, which is recognized as a major societal and public safety problem. In 2016 alone, more than thirty thousand people died in road accidents in United States, an increase of 5.6% over 2015 [1]. The economic cost of road traffic crashes was substantial, amounting to over 200 billion dollars a year [2]. Statistically, human error tops the list of factors of road accidents (causing 94% of road accidents), followed by vehicle malfunction, environmental factors and others [3]. The human error encompasses recognition error (e.g., driver’s inattention and distraction), decision error (e.g., reckless driving and misjudging others’ action), and performance error (e.g., overcompensation and poor driving skill) [3]. Driving automation is considered a solution to mitigate the human driving errors [4], [5]. A Driving Automation System (DAS) [6] usually makes use of a great variety of advanced sensors and technologies such as Light Detection and Ranging (LiDAR), Global Positioning System (GPS), 3D mapping, path planning and Electronic Controlled Units (ECUs).