Safe Merging in Mixed Traffic with Confidence

TL;DR

This paper introduces a method for safe merging in mixed traffic environments by learning human driving behavior with conformal prediction for safety guarantees, validated through real-world data and simulations.

Contribution

It presents a novel approach combining conformal prediction with control design to ensure safe merging of connected and automated vehicles with human-driven vehicles.

Findings

Conformal prediction provides theoretical safety guarantees.

The control approach effectively merges CAVs with HDVs.

Simulations demonstrate the method's efficacy.

Abstract

In this letter, we present an approach for learning human driving behavior, without relying on specific model structures or prior distributions, in a mixed-traffic environment where connected and automated vehicles (CAVs) coexist with human-driven vehicles (HDVs). We employ conformal prediction to obtain theoretical safety guarantees and use real-world traffic data to validate our approach. Then, we design a controller that ensures effective merging of CAVs with HDVs with safety guarantees. We provide numerical simulations to illustrate the efficacy of the control approach.