# Deep Learning Based Caching for Self-Driving Car in Multi-access Edge Computing

arXiv: https://arxiv.org/abs/1810.01548 · HTML: https://mlsanity.com/paper/1810.01548/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 11 of 11)

- [How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses](https://mlsanity.com/paper/2305.10862/) (2023)
- [TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networks](https://mlsanity.com/paper/2112.00633/) (2021)
- [FOVA: Offline Federated Reinforcement Learning with Mixed-Quality Data](https://mlsanity.com/paper/2512.02350/) (2025)
- [Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks](https://mlsanity.com/paper/2210.05874/) (2022)
- [Mobility and Cost Aware Inference Accelerating Algorithm for Edge Intelligence](https://mlsanity.com/paper/2312.16497/) (2023)
- [CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage](https://mlsanity.com/paper/2507.16872/) (2025)
- [Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging](https://mlsanity.com/paper/2108.01466/) (2021)
- [ViT-CAT: Parallel Vision Transformers with Cross Attention Fusion for Popularity Prediction in MEC Networks](https://mlsanity.com/paper/2210.15125/) (2022)
- [CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks](https://mlsanity.com/paper/2303.12097/) (2023)
- [High Efficiency Inference Accelerating Algorithm for NOMA-based Mobile Edge Computing](https://mlsanity.com/paper/2312.15850/) (2023)
- [A QoE-Aware Split Inference Accelerating Algorithm for NOMA-based Edge Intelligence](https://mlsanity.com/paper/2409.16537/) (2024)

## Abstract (arXiv)

Once self-driving car becomes a reality and passengers are no longer worry about it, they will need to find new ways of entertainment. However, retrieving entertainment contents at the Data Center (DC) can hinder content delivery service due to high delay of car-to-DC communication. To address these challenges, we propose a deep learning based caching for self-driving car, by using Deep Learning approaches deployed on the Multi-access Edge Computing (MEC) structure. First, at DC, Multi-Layer Perceptron (MLP) is used to predict the probabilities of contents to be requested in specific areas. To reduce the car-DC delay, MLP outputs are logged into MEC servers attached to roadside units. Second, in order to cache entertainment contents stylized for car passengers' features such as age and gender, Convolutional Neural Network (CNN) is used to predict age and gender of passengers. Third, each car requests MLP output from MEC server and compares its CNN and MLP outputs by using k-means and binary classification. Through this, the self-driving car can identify the contents need to be downloaded from the MEC server and cached. Finally, we formulate deep learning based caching in the self-driving car that enhances entertainment services as an optimization problem whose goal is to minimize content downloading delay. To solve the formulated problem, a Block Successive Majorization-Minimization (BS-MM) technique is applied. The simulation results show that the accuracy of our prediction for the contents need to be cached in the areas of the self-driving car is achieved at 98.04% and our approach can minimize delay.