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 the authors tried to solve this problem by using CARLA simulation. However, there is a suggestion that the use of CARLA simulation in research institutes and companies that research autonomous driving such as Tesla results in poor results, and that real data, not simulations, are needed in the end. Therefore, the authors propose a method to obtain real data in real-time. In order to utilize the actual data, when the sensor installed in the vehicle recognizes the dangerous situation, the embed-ded 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 and speed will be used. The authors propose a con-nectivity vehicle technology system solution, which is an important part of autonomous driv-ing, using big data-based deep learning algorithms. Connectivity autonomous driving uses big data to provide access to a high level of autonomy. A technology that provides access to a high level of autonomy is very important for vehicle development, and continuous and stable access to big data is also important for successful connectivity autonomous driving. In this study, the authors implement and extensively evaluate the system by autoware under various settings using a popular end-to-end self-driving software Autoware on NVIDIA Corporation for the development of autonomous vehicles, and evaluate the performance of Autoware on (GPUs). Autoware is a popular open-source software project that offers a complete set of autonomous driving modules that include positioning, detection, prediction, planning, and control mod-ules, which converts Windows-based sources to ROS-based sources given a source and des-tination. The authors use the popular open-source Autoware software to provide an analysis model de-sign and the development of an autonomous vehicle equipped with a system is performed. This paper proposes a UML diagram including the design of the Deep Learning Process Model Autonomous Driving Based on Big and software in it. The software is called embed-ded software. Model-based testing is a resolution for testing embedded software.