SLO-Aware Task Offloading within Collaborative Vehicle Platoons

TL;DR

This paper introduces a Bayesian Network-based framework for intelligent, proactive task offloading in autonomous vehicle platoons to ensure service level objectives like energy efficiency and data quality, improving SLO compliance.

Contribution

It presents a novel collaborative, V2V offloading approach using Bayesian Networks to proactively maintain SLOs in heterogeneous vehicle platoons.

Findings

Proactive SLO violation detection within seconds.

Effective offloading improves SLO compliance.

Framework handles large platoons efficiently.

Abstract

In the context of autonomous vehicles (AVs), offloading is essential for guaranteeing the execution of perception tasks, e.g., mobile mapping or object detection. While existing work focused extensively on minimizing inter-vehicle networking latency through offloading, other objectives become relevant in the case of vehicle platoons, e.g., energy efficiency or data quality for heavy-duty or public transport. Therefore, we aim to enforce these Service Level Objectives (SLOs) through intelligent task offloading within AV platoons. We present a collaborative framework for handling and offloading services in a purely Vehicle-to-Vehicle approach (V2V) based on Bayesian Networks (BNs). Each service aggregates local observations into a platoon-wide understanding of how to ensure SLOs for heterogeneous vehicle types. With the resulting models, services can proactively decide to offload if this promises to improve global SLO fulfillment. We evaluate the approach in a real-case setting, where vehicles in a platoon continuously (i.e., every 500 ms) interpret the SLOs of three actual perception services. Our probabilistic, predictive method shows promising results in handling large AV platoons; within seconds, it detects and resolves SLO violations through offloading.