发表机构
South Carolina State University; North Carolina A&T State University; Dakota State University（南卡罗来纳州立大学; 北卡罗来纳农工州立大学; 达科他州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究通过微观仿真发现，人类驾驶员气质对C-V2X DoS洪泛攻击的安全影响是特定且适度的，不系统性放大攻击，但主导基线风险，故障安全设计不能依赖谨慎测试群体。
AI 中文摘要
协作式与网联自动驾驶车辆（CAVs）依赖信号相位与配时（SPaT）消息通过信号交叉口；阻塞SPaT的拒绝服务（DoS）洪泛迫使CAVs进入故障安全模式。由于人类驾驶车辆共享交叉口，安全后果不仅取决于攻击和CAV故障安全策略，还取决于周围人类驾驶员的行为方式。我们通过耦合OMNeT++/INET（5G NR-V2X）和SUMO的信号走廊微观仿真来研究这一人为因素维度，扫描CAV市场渗透率（10%-90%）、四种校准的驾驶员气质（从谨慎到激进）以及两种基于标准的故障安全策略——最小风险机动（MRM）和自适应巡航控制（ACC）继续行驶回退方案，每个攻击臂与其策略匹配的无攻击基线进行差分。气质对攻击的影响是特定且适度的，而非全面放大。激进的周围环境恶化了一项指标，即继续行驶故障安全迫使附近驾驶员急刹车（p = 0.03），从接近零上升到+8次/1000车辆秒。它们似乎抑制了追尾冲突，但这只是因为洪泛清除了激进驾驶员形成的队列，因此收益在于流量而非安全。在攻击的主要特征——CAV闯红灯和交叉冲突上，气质没有可检测的影响。相反，它主导了基线风险，产生13至18倍的谨慎到激进梯度，远大于攻击本身，而攻击通过已被CAV采用堵塞的渠道起作用。人类驾驶员群体决定了交叉口的危险程度，但并未系统性地放大此攻击，因此故障安全设计不能假设谨慎的测试群体能界定风险范围。
英文摘要
Cooperative and connected automated vehicles (CAVs) rely on Signal Phase and Timing (SPaT) messages to cross signalized intersections; a denial-of-service (DoS) flood that blocks SPaT forces CAVs into a fail-safe mode. Because human-driven vehicles share the intersection, the safety consequence depends not only on the attack and the CAV fail-safe policy, but on how the surrounding human drivers behave. We investigate this human-factors dimension with a coupled OMNeT++/INET (5G NR-V2X) and SUMO microsimulation of a signalized corridor, sweeping CAV market penetration (10-90%), four calibrated driver temperaments (cautious to aggressive) and two standards-based fail-safe policies, a minimal-risk maneuver (MRM) and an adaptive cruise control (ACC) keep-driving fallback, with each attack arm differenced against its policy-matched no-attack baseline. Temperament's effect on the attack is specific and modest rather than a blanket amplification. Aggressive surroundings worsen one metric, the hard-braking a keep-driving fail-safe forces on nearby drivers (p = 0.03), rising from near zero to +8 episodes/1000 veh-s. They appear to dampen rear-end conflicts, but only because the flood clears the queues aggressive drivers build, so the gain is in flow, not safety. On the attack's primary signatures, CAV red-light running and crossing conflicts, temperament has no detectable effect. It instead dominates baseline risk, producing a 13- to 18-fold cautious-to-aggressive gradient far larger than the attack itself, which acts through a channel already congested by CAV adoption. Human driver populations determine how dangerous the intersection is but do not systematically amplify this attack, so fail-safe design cannot assume a cautious test population bounds the risk.
Comments12 pages, 6 figures, Submitted to AHFE Hawaii International Conference
