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