School of Engineering, RMIT University Melbourne, Australia.
School of Engineering, RMIT University Melbourne, Australia.
Accid Anal Prev. 2023 Jun;186:107054. doi: 10.1016/j.aap.2023.107054. Epub 2023 Apr 4.
Technological advancements in Connected and Automated Vehicles (CAVs), particularly the integration of diverse stakeholder groups (communication service providers, road operators, automakers, repairers, CAV consumers, and the general public) and the pursuit of new economic opportunities, have resulted in the emergence of new technical, legal, and social challenges. The most pressing challenge is deterring criminal behaviour in both the physical and cyber realms through the adoption of CAV cybersecurity protocols and regulations. However, the literature lacks a systematic decision tool to analyze the impact of the potential cybersecurity regulations for dynamically interacting stakeholders, and to identify the leverage points to minimise the cyber-risks. To address this knowledge gap, this study uses systems theory to develop a dynamic modelling tool to analyze the indirect consequences of potential CAVs cybersecurity regulations in the medium to long term. It is hypothesized that CAVs Cybersecurity Regulatory Framework (CRF) is the property of the entire ITS stakeholders. The CRF is modelled using the System Dynamic based Stock-and-Flow-Model (SFM) technique. The SFM is founded on five critical pillars: the Cybersecurity Policy Stack, the Hacker's Capability, Logfiles, CAV Adopters, and intelligence-assisted traffic police. It is found that decision-makers should focus on three major leverage points: establishing a CRF grounded on automakers' innovation; sharing risks in eliminating negative externalities associated with underinvestment and knowledge asymmetries in cybersecurity; and capitalising on massive CAV-generated data in CAV operations. The formal integration of intelligence analysts and computer crime investigators to strengthen traffic police capabilities is pivotal. Recommendations for automakers include data-profiteering in CAV design, production, sales, marketing, safety enhancements and enabling consumer data transparency.Furthermore, CAVs-CRF necessitate a balanced approach to the trade-off between: i) data accessibility constraints on CAV automakers and ITS service providers; ii) regulator command and control thresholds; iii) automakers' business investment protection; and iv) consumers' data privacy guard.
技术进步在互联和自动驾驶车辆(CAV)中,特别是通过采用 CAV 网络安全协议和法规,将不同利益相关者群体(通信服务提供商、道路运营商、汽车制造商、维修商、CAV 消费者和公众)整合在一起,并追求新的经济机会,产生了新的技术、法律和社会挑战。最紧迫的挑战是通过采用 CAV 网络安全协议和法规来阻止物理和网络领域的犯罪行为。然而,文献中缺乏一种系统的决策工具来分析潜在的 CAV 网络安全法规对动态交互利益相关者的影响,并确定减少网络风险的杠杆点。为了弥补这一知识空白,本研究使用系统理论开发了一种动态建模工具,以分析潜在 CAV 网络安全法规在中长期内的间接后果。假设 CAV 网络安全监管框架(CRF)是整个智能交通系统利益相关者的财产。使用基于系统动态的存量-流量模型(SFM)技术对 CRF 进行建模。SFM 基于五个关键支柱:网络安全政策组合、黑客能力、日志文件、CAV 采用者和智能辅助交通警察。研究结果表明,决策者应关注三个主要的杠杆点:建立基于汽车制造商创新的 CRF;在消除网络安全投资不足和知识不对称相关的负外部性方面共同承担风险;并利用 CAV 运营中产生的大量 CAV 数据。正式整合情报分析师和计算机犯罪调查员以加强交通警察的能力至关重要。为汽车制造商提出的建议包括在 CAV 设计、生产、销售、营销、安全增强和实现消费者数据透明度方面的数据盈利。此外,CAVs-CRF 需要在以下方面采取平衡的方法来权衡:i)CAV 汽车制造商和智能交通系统服务提供商对数据可访问性的限制;ii)监管者的指挥和控制阈值;iii)汽车制造商的商业投资保护;iv)消费者的数据隐私保护。