  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.
