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

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.