  Action anticipation, intent prediction, and proactive behavior are all
desirable characteristics for autonomous driving policies in interactive
scenarios. Paramount, however, is ensuring safety on the road --- a key
challenge in doing so is accounting for uncertainty in human driver actions
without unduly impacting planner performance. This paper introduces a
minimally-interventional safety controller operating within an autonomous
vehicle control stack with the role of ensuring collision-free interaction with
an externally controlled (e.g., human-driven) counterpart. We leverage
reachability analysis to construct a real-time (100Hz) controller that serves
the dual role of (1) tracking an input trajectory from a higher-level planning
algorithm using model predictive control, and (2) assuring safety through
maintaining the availability of a collision-free escape maneuver as a
persistent constraint regardless of whatever future actions the other car
takes. A full-scale steer-by-wire platform is used to conduct traffic weaving
experiments wherein the two cars, initially side-by-side, must swap lanes in a
limited amount of time and distance, emulating cars merging onto/off of a
highway. We demonstrate that, with our control stack, the autonomous vehicle is
able to avoid collision even when the other car defies the planner's
expectations and takes dangerous actions, either carelessly or with the intent
to collide, and otherwise deviates minimally from the planned trajectory to the
extent required to maintain safety.
