End-to-end deep learning model for steering angle control of autonomous vehicles

Abida Khanum, Chao Yang Lee, Chu Sing Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently brilliant evolutions in the machine learning research area of autonomous self-driving vehicles. Unlike a modern rule-based method, this study has been supervised on the manipulate of images end-to-end, which is deep learning. The motivation of this paper where the input to the model is the camera image and the output is the steering angle target. The model trained a Residual Neural Network (ResNet) convolutional neural network(CNN) algorithm to drive an autonomous vehicle in the simulator. Therefore, the model trained and simulation are conducted using the UDACITY platform. The simulator has two choices one is the training and the second one is autonomous. The autonomous has two tracks track_1 considered as simple and track_2 complex as compare to track_1. In our paper, we used track_1 for autonomous driving in the simulator. The training option gives the recorded dataset its control through the keyboard in the simulator. We collected about 11655 images (left, center, right) with four attributes (steering, throttle, brake, speed) and also images dataset stored in a folder and attributes dataset save as CSV file in the same path. The stored raw images and steering angle data set used in this method. We divided 80-20 data set for training and Validation as shown in Table I. Images were sequentially fed into the convolutional neural network (ResNet)to predict the driving factors for making end planning decisions and execution of autonomous motion of vehicles. The loss value of the proposed model is 0.0418 as shown in Figure 2. The method trained takes succeeded precision of 0.81% is good consent with expected performance.

Original languageEnglish
Title of host publicationProceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages189-192
Number of pages4
ISBN (Electronic)9781728193625
DOIs
Publication statusPublished - 2020 Nov
Event2020 International Symposium on Computer, Consumer and Control, IS3C 2020 - Taichung, Taiwan
Duration: 2020 Nov 132020 Nov 16

Publication series

NameProceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020

Conference

Conference2020 International Symposium on Computer, Consumer and Control, IS3C 2020
Country/TerritoryTaiwan
CityTaichung
Period20-11-1320-11-16

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Optimization
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Computer Science Applications
  • Energy Engineering and Power Technology
  • Artificial Intelligence

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