Given the promising future of autonomous vehicles, it is foreseeable that self-driving cars will soon emerge as the predominant mode of transportation. While autonomous vehicles offer enhanced efficiency, they remain vulnerable to external attacks. In this research, we sought to investigate the potential impact of cyberattacks on traffic patterns. To achieve this, we conducted simulations where cyberattacks were simulated on connected vehicles by disseminating false information to either a single vehicle or vehicle platoons. The primary objective of this research is to assess the cybersecurity challenges confronting connected and automated vehicles and propose practical solutions to minimize the adverse effects of malicious external information. In the simulation, we have implemented an innovative car-following model for the simulation of connected self-driving vehicles. This model continually monitors data received from preceding vehicles and optimizes various actions, such as acceleration, and deceleration, with the aim of maximizing overall traffic efficiency and safety.

翻译：鉴于自动驾驶汽车前景广阔，可预见无人驾驶汽车将很快成为主要交通方式。虽然自动驾驶汽车能提升效率，但仍易受外部攻击。本研究旨在探究网络攻击对交通流模式的潜在影响。为此，我们通过向单车或车辆编队传播虚假信息来模拟网联汽车遭受网络攻击的情景。本研究的主要目标是评估网联自动驾驶汽车面临的网络安全挑战，并提出切实可行的解决方案以最小化恶意外部信息的不利影响。在仿真中，我们为网联自动驾驶汽车实现了一种创新的跟驰模型。该模型持续监测前车传输的数据，并通过优化加速、减速等动作来最大化整体交通效率与安全性。