School of Transportation, Southeast University, 2 Si pai lou, Nanjing, 210096, PR China.
Accid Anal Prev. 2018 Dec;121:148-156. doi: 10.1016/j.aap.2018.09.016. Epub 2018 Sep 21.
Connected and automated vehicle (CAV) has been a remarkable focal point in recent years, since it is recognized as a potential method to reduce traffic congestion, emission and accident. However, the connectivity function makes CAVs vulnerable to cyber-attacks. An intuitive method to defend cyber-attacks on CAVs is that if the error between expected and measured behaviors exceeds a predetermined threshold, a security scheme should be activated. This study investigates another type of cyber-attack, denoted as slight attacks, in which the communicated data of CAVs are randomly deviated from the actual ones and deviations do not exceed the threshold. The primary objective is to evaluate the influence of slight cyber-attacks on longitudinal safety of CAVs. An empirical CAV model is first utilized to describe vehicle dynamics and generate trajectory data. A rear-end collision risk index (RCRI) derived from safe stopping distance is used to establish relation between longitudinal safety and trajectory data. Two attacked factors, communicated positions and speeds from preceding vehicles are tested. Extensive simulations are conducted and parameters are also tested via sensitivity analysis. Results indicate that (1) when one CAV is under slight cyber-attacks, it is more dangerous if communicated positions are attacked than speeds; (2) when multi CAVs are under attacked, it is possible that a situation with more vehicles under attack at a low severity may be more dangerous than that with fewer vehicles but under attack at a high severity; (3) the impact of slight cyber-attacks on deceleration period is more serious compared to acceleration period. The findings of this study provide useful suggestion for defending cyber-attacks on CAVs and improving longitudinal safety in the future.
联网和自动驾驶汽车（CAV）是近年来的一个显著焦点，因为它被认为是减少交通拥堵、排放和事故的潜在方法。然而，连接功能使 CAV 容易受到网络攻击。防御 CAV 网络攻击的一种直观方法是，如果预期行为与测量行为之间的误差超过预定阈值，则应激活安全方案。本研究调查了另一种类型的网络攻击，称为轻微攻击，其中 CAV 的通信数据随机偏离实际数据，且偏差不超过阈值。主要目的是评估轻微网络攻击对 CAV 纵向安全的影响。首先利用经验 CAV 模型来描述车辆动力学并生成轨迹数据。从安全停车距离得出的追尾碰撞风险指数（RCRI）用于建立纵向安全与轨迹数据之间的关系。测试了两个受攻击因素，即来自前车的通信位置和速度。进行了广泛的模拟，并通过敏感性分析测试了参数。结果表明：（1）当一辆 CAV 受到轻微网络攻击时，通信位置受到攻击比速度受到攻击更危险；（2）当多辆 CAV 受到攻击时，受攻击车辆数量较少但攻击严重程度较高的情况可能比受攻击车辆数量较多但攻击严重程度较低的情况更危险；（3）与加速阶段相比，轻微网络攻击对减速阶段的影响更严重。本研究的结果为防御 CAV 网络攻击和提高未来纵向安全提供了有用的建议。
