  Intelligent intersection managers can improve safety by detecting dangerous
drivers or failure modes in autonomous vehicles, warning oncoming vehicles as
they approach an intersection. In this work, we present FailureNet, a recurrent
neural network trained end-to-end on trajectories of both nominal and reckless
drivers in a scaled miniature city. FailureNet observes the poses of vehicles
as they approach an intersection and detects whether a failure is present in
the autonomy stack, warning cross-traffic of potentially dangerous drivers.
FailureNet can accurately identify control failures, upstream perception
errors, and speeding drivers, distinguishing them from nominal driving. The
network is trained and deployed with autonomous vehicles in the MiniCity.
Compared to speed or frequency-based predictors, FailureNet's recurrent neural
network structure provides improved predictive power, yielding upwards of 84%
accuracy when deployed on hardware.
