# Lessons Learned from Accident of Autonomous Vehicle Testing: An Edge Learning-aided Offloading Framework

arXiv: https://arxiv.org/abs/2006.15382 · HTML: https://mlsanity.com/paper/2006.15382/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2020.

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

- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [Generative AI for Testing of Autonomous Driving Systems: A Survey](https://mlsanity.com/paper/2508.19882/) (2025)

## Abstract (arXiv)

This letter proposes an edge learning-based offloading framework for autonomous driving, where the deep learning tasks can be offloaded to the edge server to improve the inference accuracy while meeting the latency constraint. Since the delay and the inference accuracy are incurred by wireless communications and computing, an optimization problem is formulated to maximize the inference accuracy subject to the offloading probability, the pre-braking probability, and data quality. Simulations demonstrate the superiority of the proposed offloading framework.