The Impact of Cybersecurity Attacks on Human Trust in Autonomous Vehicle Operations

Cherin Lim, David Prendez, Linda Ng Boyle, Prashanth Rajivan
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Abstract

ObjectiveThis study examines the extent to which cybersecurity attacks on autonomous vehicles (AVs) affect human trust dynamics and driver behavior.BackgroundHuman trust is critical for the adoption and continued use of AVs. A pressing concern in this context is the persistent threat of cyberattacks, which pose a formidable threat to the secure operations of AVs and consequently, human trust.MethodA driving simulator experiment was conducted with 40 participants who were randomly assigned to one of two groups: (1) Experience and Feedback and (2) Experience-Only. All participants experienced three drives: Baseline, Attack, and Post-Attack Drive. The Attack Drive prevented participants from properly operating the vehicle in multiple incidences. Only the “Experience and Feedback” group received a security update in the Post-Attack drive, which was related to the mitigation of the vehicle’s vulnerability. Trust and foot positions were recorded for each drive.ResultsFindings suggest that attacks on AVs significantly degrade human trust, and remains degraded even after an error-less drive. Providing an update about the mitigation of the vulnerability did not significantly affect trust repair.ConclusionTrust toward AVs should be analyzed as an emergent and dynamic construct that requires autonomous systems capable of calibrating trust after malicious attacks through appropriate experience and interaction design.ApplicationThe results of this study can be applied when building driver and situation-adaptive AI systems within AVs.
网络安全攻击对自动驾驶汽车运行中人类信任的影响
背景人类信任对于自动驾驶汽车的采用和持续使用至关重要。在此背景下,一个亟待解决的问题是持续存在的网络攻击威胁,这对自动驾驶汽车的安全运行构成了巨大威胁,从而也影响了人类的信任。方法对 40 名参与者进行了驾驶模拟器实验,他们被随机分配到两组中的一组:(1)体验和反馈组;(2)仅体验组。所有参与者都体验了三次驾驶:基准驾驶、攻击驾驶和攻击后驾驶。在攻击驾驶中,参与者多次无法正确操作车辆。只有 "体验和反馈 "组在 "攻击后驾驶 "中获得了安全更新,这与车辆漏洞的缓解有关。结果研究结果表明,对自动驾驶汽车的攻击会大大降低人类的信任度,即使在无差错驾驶之后,信任度仍然会下降。结论对自动驾驶汽车的信任应被分析为一种新兴和动态的结构,需要自主系统能够在受到恶意攻击后通过适当的经验和交互设计来校准信任。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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