Exploring Energy Impacts of Cyberattacks on Adaptive Cruise Control Vehicles
Tianyi Li, Benjamin Rosenblad, Shian Wang, Mingfeng Shang, Raphael Stern
Abstract
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.