Regular: Attacker-Induced Traffic Flow Instability in a Stream of Semi-Automated Vehicles

Abstract

We show that a stream of automated vehicles traveling along the highway can be destabilized to catastrophic effect through modification of the control laws of individual vehicles. Specifically, one active attacker who introduces errors, in addition to one or many passive attackers who amplify the error, may, by the modification of a single parameter, induce oscillatory traffic jams that cause delay, driver discomfort, excess energy expenditure, and increased risk of accidents that could result in serious injury or death. We determine the conditions under which an attacker(s) is able to violate the primary design criterion of automated vehicle streams, known as string stability, to guarantee system instability. Furthermore, we prove that once the stream has been destabilized it will continually deviate from the desired state, even in the absence of additional input to the system—i.e. the jammed condition will self-perpetuate. Through a comparison with a behavioral human driver model, this work demonstrates that automated vehicle systems are more vulnerable to disruption than their non-automated counterparts. The postulated attack is demonstrated on a scaled system and identification of attackers is discussed.