Security experimental framework of trajectory planning for autonomous vehicles
In the contemporary landscape, autonomous vehicles (AVs) have emerged as a prominent technological advancement globally. Despite their widespread adoption, significant hurdles remain, with security standing out as a critical concern. The potential for attacks within AV networks, exemplified by the T...
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Published in | International journal of intelligent networks Vol. 5; pp. 315 - 324 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
2024
KeAi Communications Co., Ltd |
Subjects | |
Online Access | Get full text |
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Summary: | In the contemporary landscape, autonomous vehicles (AVs) have emerged as a prominent technological advancement globally. Despite their widespread adoption, significant hurdles remain, with security standing out as a critical concern. The potential for attacks within AV networks, exemplified by the Trajectory Privacy Attack on Autonomous Driving (T-PAAD), underscores the urgency for robust security measures. Unfortunately, existing simulations for preemptively assessing the T-PAAD attack's impact are scarce. This paper introduces the Security Experimental Framework for Autonomous Vehicles (SEFAV), designed to address this gap by providing a versatile platform for simulating security scenarios in AV environments. SEFAV is cross-platform and compatible with different operating systems such as Windows and Linux, enhancing accessibility for researchers and practitioners. Our primary focus lies in showcasing the T-PAAD attack within our framework, highlighting its efficacy in evaluating and fortifying AV security. |
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ISSN: | 2666-6030 2666-6030 |
DOI: | 10.1016/j.ijin.2024.08.003 |