Safe Merging in Mixed Traffic with Confidence

Fuente: arXiv
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Autori principali: Bang, Heeseung, Dave, Aditya, Malikopoulos, Andreas A.
Natura: Preprint
Pubblicazione: 2024
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author Bang, Heeseung
Dave, Aditya
Malikopoulos, Andreas A.
author_facet Bang, Heeseung
Dave, Aditya
Malikopoulos, Andreas A.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2403_05742
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Safe Merging in Mixed Traffic with Confidence
Bang, Heeseung
Dave, Aditya
Malikopoulos, Andreas A.
Systems and Control
Robotics
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.
title Safe Merging in Mixed Traffic with Confidence
topic Systems and Control
Robotics
url https://arxiv.org/abs/2403.05742