Title
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.
Year
DOI
Venue
2017
10.1109/DSN.2017.61
2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
Keywords
Field
DocType
attacker-induced traffic flow instability,semiautomated vehicle stream,active attacker,passive attackers,oscillatory traffic jams,string stability,system instability,behavioral human driver model,automated vehicle systems
Traffic flow,Computer science,Instability,Energy expenditure,Real-time computing
Conference
ISSN
ISBN
Citations 
1530-0889
978-1-5386-0543-1
0
PageRank 
References 
Authors
0.34
10
6
Name
Order
Citations
PageRank
Daniel D. Dunn100.34
Samuel A. Mitchell211.02
Imran Sajjad300.68
Ryan M. Gerdes44112.72
Rajnikant Sharma5338.76
Ming Li6177084.74