# On Infusing Reachability-Based Safety Assurance within Probabilistic Planning Frameworks for Human-Robot Vehicle Interactions

## TL;DR

This paper presents a real-time reachability-based safety controller for autonomous vehicles that ensures collision avoidance in interactive scenarios with human-driven cars, maintaining safety without significantly compromising planned trajectories.

## Contribution

It introduces a minimally-interventional safety controller using reachability analysis within an autonomous vehicle control stack for collision-free human-robot interactions.

## Key findings

- Successfully avoids collisions in traffic weaving experiments.
- Maintains safety even when human driver actions are unpredictable or dangerous.
- Operates at 100Hz in real-time to ensure safety constraints are always met.

## Abstract

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.