Modeling and evaluation of cyberattack impacts on multi-dimensional performance of connected automated vehicle platoons

Connected Automated Vehicle (CAV) platoons, while promising significant improvements in traffic systems, are vulnerable to cyberattacks, yet the quantitative safety impacts of diverse attack modalities remain poorly understood. This paper investigates the underlying mechanisms of how various cyberattack types compromise the cooperative car-following dynamics and safety of CAV platoons. We introduce a generic car-following model that explicitly embeds a bi-directional communication topology and instantiate it via the Intelligent Driver Model (IDM). A dedicated dynamic simulation framework is then constructed to systematically quantify and compare the safety impacts of six cyberattack types manipulating vehicle kinematics (speed, position, acceleration). The platoon’s safety performance was rigorously evaluated by categorizing and analyzing various cyberattack types, and assessing their impacts across different platoon sizes, attack durations, and targeting schemes, including both single and coordinated multi-vehicle attacks. Results demonstrate that deceleration attacks most severely degrade platoon stability, while acceleration attacks precipitate the most acute collision risks. Coordinated attacks on intermediate vehicles were found to dramatically escalate collision probability. The influence of platoon size and attack duration on risk propagation is complex, with system adaptability showing potential for risk mitigation in specific contexts. Critically, the findings reveal a decoupling of string stability from collision risk and show that systemic threats from coordinated attacks significantly outweigh those from single-point intrusions. These insights provide a theoretical basis for designing differentiated cybersecurity defenses and lay the groundwork for developing robust detection and control strategies resilient to high-risk, coordinated cyber-physical threats in CAV platoons.