Is your commute driving you crazy?: a study of misbehavior in vehicular platoons

Abstract

Traffic is not only a source of frustration but also a leading cause of death for people under 35 years of age. Recent research has focused on how driver assistance technologies can be used to mitigate traffic fatalities and create more enjoyable commutes. In this work, we consider cooperative adaptive cruise control (CACC) or platooning, a driver assistance technology that controls the speed of vehicles and inter-vehicle spacing. CACC equipped cars use radar to fine tune inter-vehicle spacing and dedicated short-range communication (DSRC) to collaboratively accelerate and decelerate. Platooning can reduce fuel consumption by over 5% and increases the density of cars on a highway. Previous work on platooning has focused on proving string stability, which guarantees that the error between cars does not grow with the length of a platoon, but little work has considered the impact an attacker can have on a platoon. To design safe distributed controllers and networks it is essential to understand the possible attacks that could be mounted against platoons. In this work, we design a set of insider attacks and abnormal behaviors that occur in a platoon of cars. For example, we introduce the collision induction attack where an attacker exploits the platoon controller to cause a high-speed accident with the car following it. To mitigate these insider attacks we design a model-based detection scheme that leverages the broadcast nature of DSRC. Each car uses DSRC messages from other cars in the platoon to model the expected behavior of the car directly preceding it. If the expected behavior and actual behavior differ the monitoring vehicle switches to non-cooperative ACC, relying solely on radar, to mitigate the impact of the attack. We show that our detection scheme is able to detect many of our proposed insider attacks and when combined with a well designed ACC controller can avoid collisions. We propose combining our detection scheme with a global reputation scheme to detect when a car is malicious or needs maintenance.