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While much effort has been invested in studies of traffic flow as a physicsproblem, two emerging trends in technology have broadened the subject for newinvestigations. The first trend is the development of self-driving vehicles.This highly-anticipated shift from human- to autonomous-drivers is expected tooffer substantial benefits for traffic throughput by streamlining large-scalecollective behavior. The second trend is the widespread hacking ofInternet-connected devices, which as of 2015, includes vehicles. While thefirst proof-of-concept automobile hack was done at the single-vehicle scale,undesirable collective effects can easily arise if this activity becomes morecommon. Motivated by these two trends, we explore the phenomena that arise inan active matter model with lanes and lane-changing behavior. Our modelincorporates a simplified minimal description of essential differences betweenhuman- and autonomous-drivers. We study the emergent collective behavior as thepopulation of vehicles shifts from all-human to all-autonomous. Within thecontext of our model, we explore a worst-case scenario where Internet-connectedautonomous vehicles are disabled simultaneously and \textit{en masse}. Ourapproach reveals a model-independent role for percolation in interpreting theresults. A broad lesson our work highlights is that seemingly minor maliciousactivity can ultimately have major impacts when magnified through the action ofcollective behavior.

Collective behavior and emergent risks in a model of human- and autonomously-driven vehicles