CRII: CHS: Training and Feedback Systems to Improve Vehicle Cybersecurity

CRII：CHS：提高车辆网络安全的培训和反馈系统

• 批准号: 1755795
• 负责人: Shannon Roberts
• 金额: $ 17.5万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755795&HistoricalAwards=false
• 关键词: CRII; CHS; Training; Feedback; Systems

Although modern vehicles have many computing components, they are not designed with cybersecurity in mind. Consequently, they are susceptible to cyberattacks. For example, in 2015 two hackers remotely exploited a vulnerability in an SUV's entertainment system to send the vehicle into a ditch. Though many technological solutions to improve vehicle cybersecurity have been proposed, the PI argues real progress toward achieving that goal will require including the human (driver) in the loop. Thus, this research will combine findings from cybersecurity and transportation safety to develop training and in-vehicle messaging systems to improve drivers' awareness of and responses to cyberattacks, which ultimately should help reduce crash-related costs and potentially save lives. The effectiveness of the new systems will be tested in a driving simulator study. Project outcomes will yield quantitative evidence as to how drivers respond to unintended events that occur in their vehicles. More importantly, the work will create systems that can warn drivers of dangers associated with vehicle hacking, and provide information on how these dangerous situations can be mitigated. Similar systems will be applicable in other transportation safety contexts (e.g., automated driving). This research will proceed in two phases. Phase I will focus on the iterative development of the training and in-vehicle message systems using methods essential to the human-centered design process (e.g., interviews and co-design sessions). Phase II will evaluate the training and new in-vehicle messaging systems though a driving simulator study. During the experiment, drivers will be exposed to various situations that are indicative of a vehicle cyberattack while their driving behavior is recorded. Drivers' responses to the scenarios according to quantitative measures of driving performance along with their subjective evaluation of the systems' effectiveness will be used to determine the extent to which the training and/or in-vehicle messages have a positive impact on driver performance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

尽管现代汽车有许多计算组件，但它们在设计时并没有考虑到网络安全。因此，他们很容易受到网络攻击。例如，2015年，两名黑客远程利用一辆SUV娱乐系统中的漏洞将车辆送入沟中。尽管已经提出了许多提高车辆网络安全的技术解决方案，但PI认为，实现这一目标的真正进展将需要将人(司机)纳入环路。因此，这项研究将结合网络安全和交通安全的研究结果，开发培训和车载信息系统，以提高司机对网络攻击的认识和反应，最终应有助于降低与碰撞相关的成本，并有可能拯救生命。新系统的有效性将在驾驶模拟器研究中进行测试。项目成果将提供量化证据，说明司机如何应对车辆中发生的意外事件。更重要的是，这项工作将创建能够警告司机与车辆黑客相关的危险的系统，并提供如何缓解这些危险情况的信息。类似的系统将适用于其他交通安全环境(例如，自动驾驶)。这项研究将分两个阶段进行。第一阶段将侧重于使用对以人为中心的设计过程(如面谈和联合设计会议)至关重要的方法，反复开发培训和车载信息系统。第二阶段将通过驾驶模拟器研究来评估培训和新的车载信息系统。在实验期间，司机将暴露在各种情况下，这些情况表明他们受到了车辆的网络攻击，同时他们的驾驶行为被记录下来。司机根据驾驶表现的量化测量对场景的反应，以及他们对系统有效性的主观评估，将被用来确定培训和/或车载信息对司机表现产生积极影响的程度。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Simulator Study on Drivers’ Response and Perception Towards Vehicle Cyberattacks

驾驶员对车辆网络攻击的反应和感知的模拟器研究

• DOI: 10.1177/1071181319631310
• 发表时间: 2019
• 期刊: Proceedings of the Human Factors and Ergonomics Society Annual Meeting
• 作者: Zhang, Fangda;Petit, Jonathan;Roberts, Shannon C.
• 通讯作者: Roberts, Shannon C.