# An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments

## TL;DR

This paper introduces a real-time, reachability-based safety framework for autonomous vehicles navigating unknown environments, ensuring safety despite environmental uncertainties and sensor limitations.

## Contribution

It presents a novel Hamilton-Jacobi reachability approach that offers provable safety guarantees in real-time for autonomous navigation in unknown settings.

## Key findings

- Successfully demonstrated safety guarantees in simulation.
- Validated approach on hardware with vision-based planners.
- Achieved real-time safety analysis compatible with various sensors.

## Abstract

Real-world autonomous vehicles often operate in a priori unknown environments. Since most of these systems are safety-critical, it is important to ensure they operate safely in the face of environment uncertainty, such as unseen obstacles. Current safety analysis tools enable autonomous systems to reason about safety given full information about the state of the environment a priori. However, these tools do not scale well to scenarios where the environment is being sensed in real time, such as during navigation tasks. In this work, we propose a novel, real-time safety analysis method based on Hamilton-Jacobi reachability that provides strong safety guarantees despite environment uncertainty. Our safety method is planner-agnostic and provides guarantees for a variety of mapping sensors. We demonstrate our approach in simulation and in hardware to provide safety guarantees around a state-of-the-art vision-based, learning-based planner.