  For autonomous vehicles to safely share the road with human drivers,
autonomous vehicles must abide by specific "road rules" that human drivers have
agreed to follow. "Road rules" include rules that drivers are required to
follow by law -- such as the requirement that vehicles stop at red lights -- as
well as more subtle social rules -- such as the implicit designation of fast
lanes on the highway. In this paper, we provide empirical evidence that
suggests that -- instead of hard-coding road rules into self-driving algorithms
-- a scalable alternative may be to design multi-agent environments in which
road rules emerge as optimal solutions to the problem of maximizing traffic
flow. We analyze what ingredients in driving environments cause the emergence
of these road rules and find that two crucial factors are noisy perception and
agents' spatial density. We provide qualitative and quantitative evidence of
the emergence of seven social driving behaviors, ranging from obeying traffic
signals to following lanes, all of which emerge from training agents to drive
quickly to destinations without colliding. Our results add empirical support
for the social road rules that countries worldwide have agreed on for safe,
efficient driving.
