Title: Traffic models with adversarial vehicle behaviour,
Abstract: We examine the impact of adversarial actions on vehicles in traffic. Current
advances in assisted/autonomous driving technologies are supposed to reduce the
number of casualties, but this seems to be desired despite the recently proved
insecurity of in-vehicle communication buses or components. Fortunately to some
extent, while compromised cars have become a reality, the numerous attacks
reported so far on in-vehicle electronics are exclusively concerned with
impairments of a single target. In this work we put adversarial behavior under
a more complex scenario where driving decisions deluded by corrupted
electronics can affect more than one vehicle. Particularly, we focus our
attention on chain collisions involving multiple vehicles that can be amplified
by simple adversarial interventions, e.g., delaying taillights or falsifying
speedometer readings. We provide metrics for assessing adversarial impact and
consider safety margins against adversarial actions. Moreover, we discuss
intelligent adversarial behaviour by which the creation of rogue platoons is
possible and speed manipulations become stealthy to human drivers. We emphasize
that our work does not try to show the mere fact that imprudent speeds and
headways lead to chain-collisions, but points out that an adversary may favour
such scenarios (eventually keeping his actions stealthy for human drivers) and
further asks for quantifying the impact of adversarial activity or whether
existing traffic regulations are prepared for such situations.