A Study on Deep Learning Model Autonomous Driving Based on Big Data

Abstract

Autonomous driving requires a large amount of data to improve performance, and we tried to solve this problem by using CARLA simulation. In order to utilize the actual data, when the sensor installed in the vehicle recognizes the dangerous situation, the embedded device detects and judges the danger 5-10 seconds in advance, and the acquired various dangerous situation data is sent to the iCloud(server) for retraining with new data. Over time, the learning model's performance gets better and more perfect. The deep learning model used for training is a detection model based on a convolution neural network (CNN), and a YOLO model that shows optimal detection performance. We propose a connectivity vehicle technology system solution, which is an important part of autonomous driving, using big data-based deep learning algorithms. In this study, We implement and extensively evaluate the system by auto ware under various settings using a popular end-to-end self-driving software Autoware on NVIDIA Corporation for the development of autonomous vehicles.