Human driver behavior shapes safety but not attack risk at intersections
Human Driver Temperament and the Safety Impact of a C-V2X Denial-of-Service Flooding Attack in Mixed-Autonomy Traffic
Human-Computer Interaction
Summary
Connected and automated vehicles depend on traffic signals to cross intersections safely. If an attacker blocks these signals, automated cars switch to safety modes that can affect nearby human drivers differently depending on their driving style. The authors found that whether drivers are cautious or aggressive changes overall safety risks but does not make the attack itself worse. This means safety systems should consider real human driving behaviors rather than assuming everyone drives cautiously.
What this means in practice
- •For traffic safety planners: Design traffic signal fail-safe policies that account for different driver temperaments to improve intersection safety during signal attacks.
- •For automotive system engineers: Develop adaptive fail-safe strategies for connected automated vehicles that mitigate safety impacts when communication signals are disrupted.
Authors
Rasheed Bello, Gurcan Comert, Varghese Vaidyan, Akinbobola Jegede, Vijay Bendigeri, Judith Mwakalonge
Abstract
Cooperative and connected automated vehicles (CAVs) rely on Signal Phase and Timing (SPaT) messages to cross signalized intersections; a denial-of-service (DoS) flood that blocks SPaT forces CAVs into a fail-safe mode. Because human-driven vehicles share the intersection, the safety consequence depends not only on the attack and the CAV fail-safe policy, but on how the surrounding human drivers behave. We investigate this human-factors dimension with a coupled OMNeT++/INET (5G NR-V2X) and SUMO microsimulation of a signalized corridor, sweeping CAV market penetration (10-90%), four calibrated driver temperaments (cautious to aggressive) and two standards-based fail-safe policies, a minimal-risk maneuver (MRM) and an adaptive cruise control (ACC) keep-driving fallback, with each attack arm differenced against its policy-matched no-attack baseline. Temperament's effect on the attack is specific and modest rather than a blanket amplification. Aggressive surroundings worsen one metric, the hard-braking a keep-driving fail-safe forces on nearby drivers (p = 0.03), rising from near zero to +8 episodes/1000 veh-s. They appear to dampen rear-end conflicts, but only because the flood clears the queues aggressive drivers build, so the gain is in flow, not safety. On the attack's primary signatures, CAV red-light running and crossing conflicts, temperament has no detectable effect. It instead dominates baseline risk, producing a 13- to 18-fold cautious-to-aggressive gradient far larger than the attack itself, which acts through a channel already congested by CAV adoption. Human driver populations determine how dangerous the intersection is but do not systematically amplify this attack, so fail-safe design cannot assume a cautious test population bounds the risk.