# SLO-Aware Task Offloading within Collaborative Vehicle Platoons

arXiv: https://arxiv.org/abs/2409.17667 · HTML: https://mlsanity.com/paper/2409.17667/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2024.

## Built on by (most cited first, 2 of 2)

- [Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices](https://mlsanity.com/paper/2510.06882/) (2025)
- [Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI](https://mlsanity.com/paper/2607.20937/) (2026)

## Abstract (arXiv)

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.