The emergence of automated vehicles (AVs) with driver-assist features, such as adaptive cruise control (ACC) and other automated driving capabilities, promises a bright future for transportation systems. However, these emerging features also introduce the possibility of cyberattacks. A select number of ACC vehicles could be compromised to drive abnormally, causing a network-wide impact on congestion and fuel consumption. In this study, we first introduce two types of candidate attacks on ACC vehicles: malicious attacks on vehicle control commands and false data injection attacks on sensor measurements. Then, we examine the energy impacts of these candidate attacks on distinct traffic conditions involving both free flow and congested regimes to get a sense of how sensitive the flow is to these candidate attacks. Specifically, the widely used VT-Micro model is adopted to quantify vehicle energy consumption. We find that the candidate attacks introduced to ACC or partially automated vehicles may only adversely impact the fuel consumption of the compromised vehicles and may not translate to significantly higher emissions across the fleet.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Exploring Energy Impacts of Cyberattacks on Adaptive Cruise Control Vehicles


    Contributors:


    Publication date :

    2023-06-04


    Size :

    3197279 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Potential Cyberattacks on Automated Vehicles

    Petit, Jonathan | Online Contents | 2015


    Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control

    Mahdinia, Iman / Arvin, Ramin / Khattak, Asad J. et al. | Transportation Research Record | 2020


    Potential Cyberattacks on Automated Vehicles

    Petit, Jonathan / Shladover, Steven E. | IEEE | 2015



    Cyberattacks and Countermeasures for Intelligent and Connected Vehicles

    Luo, Feng / Hou, Shuo | SAE Technical Papers | 2019