1 research outputs found
On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions
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 while respecting
static obstacles such as a road boundary wall. We leverage reachability
analysis to construct a real-time (100Hz) controller that serves the dual role
of (i) tracking an input trajectory from a higher-level planning algorithm
using model predictive control, and (ii) assuring safety by 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
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.Comment: arXiv admin note: text overlap with arXiv:1812.1131