Deep Encoder-Decoder Network for Lane-Following on Autonomous Vehicle

Abida Khanum, Chao Yang Lee, Chu Sing Yang

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

Abstract

Nowadays there is a vast interest in a self-driving car from both academia and industry. The main reason behind recently enormous progress in deep learning approaches for an autonomous vehicle. The main objective of this research is to propose a deep hybrid encoder-decoder network with input multi-modal data to predict the decision-making task. Therefore, the proposed approaches are tested by both real and simulation data but in the real data single camera image and simulator data three-camera image data. The proposed method analyzes the effects of input data. The experiment results in analyses in terms of Computational time as-well-as parameters in which values of the steering wheel and brake both real and simulated data are (6ms and 9ms) respectively. The analysis shows that our method performs well in driving action prediction.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages583-584
Number of pages2
ISBN (Electronic)9781665470506
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 - Taipei, Taiwan
Duration: 2022 Jul 62022 Jul 8

Publication series

NameProceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Conference

Conference2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Country/TerritoryTaiwan
CityTaipei
Period22-07-0622-07-08

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Media Technology
  • Health Informatics
  • Instrumentation

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