SPARC: Prediction-Based Safe Control for Coupled Controllable and Uncontrollable Agents with Conformal Predictions

TL;DR

SPARC is a novel framework that uses conformal prediction and control barrier functions to ensure safe control of systems interacting with unknown, coupled uncontrollable agents, demonstrated in autonomous driving with pedestrians.

Contribution

We introduce SPARC, a new safe control framework that accounts for coupled dynamics and uncertainty using conformal prediction and CBFs, providing high-confidence safety guarantees.

Findings

SPARC effectively predicts agent behavior with quantified uncertainty.

The framework guarantees safety with high confidence in autonomous driving scenarios.

Experimental results demonstrate robust safety performance with pedestrians.

Abstract

We investigate the problem of safe control synthesis for systems operating in environments with uncontrollable agents whose dynamics are unknown but coupled with those of the controlled system. This scenario naturally arises in various applications, such as autonomous driving and human-robot collaboration, where the behavior of uncontrollable agents, like pedestrians, cannot be directly controlled but is influenced by the actions of the autonomous vehicle or robot. In this paper, we present SPARC (Safe Prediction-Based Robust Controller for Coupled Agents), a novel framework designed to ensure safe control in the presence of coupled uncontrollable agents. SPARC leverages conformal prediction to quantify uncertainty in data-driven prediction of agent behavior. Particularly, we introduce a joint distribution-based approach to account for the coupled dynamics of the controlled system and uncontrollable agents. By integrating the control barrier function (CBF) technique, SPARC provides provable safety guarantees at a high confidence level. We illustrate our framework with a case study involving an autonomous driving scenario with walking pedestrians.