一种网络攻击下网联自动车的改进换道模型

An Improved Lane-changing Model for Connected Automated Vehicles Under Cyberattacks

摘要:

网联自动车被认为可以提升交通效率、保证行车安全并节约能源，但是由于无线通信系统的开放性，网联自动车很容易受到网络攻击的威胁。现有的研究主要集中于网络攻击的种类及过程，并评估该攻击对车辆纵向行为的影响。本文旨在研究网络攻击对车辆横向行为的影响，即对网络攻击下的换道行为进行研究。通过对经典的跟驰模型智能驾驶员模型（Intelligent Driver Model， IDM）和经典的换道模型最小化换道总制动模型（Minimizing Overall Braking Induced by Lane changes， MOBIL）进行改进，提出了一种扩展换道模型（Extended Lane-Changing model， ELC），来对网络攻击影响下的车辆换道行为进行建模分析。最后通过仿真实验，说明了不同的恶意网络攻击对车辆换道行为的影响。结果表明，网络攻击会显著影响车辆的换道决策，并导致异常驾驶行为。

Abstract:

The connected automated vehicle （CAV） is promising to enhance traffic efficiency， traveling safety， and energy savings. However， due to the open wireless communication， the CAV is vulnerable to cyber threats. Existing studies mainly focus on surveying related cyberattacks and evaluating the impact of attacks on vehicular longitudinal behaviors on a single lane. This paper aims to investigate the effects of cyberattacks on vehicular lateral behaviors on a two-lane highway， i.e.， the lane-changing （LC） behaviors under cyberattacks. Based on a classical lane-changing model--minimizing overall braking induced by lane changes （MOBIL） model， and a classical car-following model--Intelligent Driver Model （IDM）， this study proposes an extended lane-changing model （ELC） which can model CAV's lane-changing behaviors under cyberattacks. At the end， simulations are conducted to illustrate the impact of different malicious attacks on vehicles' LC movements. Results show that cyberattacks can imperil the LC maneuvers and lead to abnormal driving behaviors.