An Improved Lane-Changing Model for Connected Automated Vehicles under Cyberattacks

Existing studies focus on surveying cyberattacks and evaluating their impacts on vehicular longitudinal behaviors on a single lane. This paper investigates the effects of cyberattacks on vehicular lateral behaviors on a two-lane highway, i.e., the lane-changing behaviors under cyberattacks. The lane change is a complex maneuver, which requires integrating neighbor vehicles' dynamical parameters such as velocity, position and/or acceleration. This study introduces a car-following model to derive vehicles' dynamical parameters. Specifically, this study adopts the Intelligent Driver Model (IDM) to characterize the car-following movements. Then, incorporating a classical lane-changing model, i.e., minimizing overall braking induced by lane changes (MOBIL) model, this study proposes an extended lane-changing model with cyberattacks to obtain the decision-making conditions. Finally, simulations are conducted to illustrate the impact of different malicious attacks such as falsified acceleration, speed, and position on vehicles' movements. These results demonstrate that cyberattacks could cause traffic congestion, low traffic efficiency, and collisions.