Deep Learning-Based Decision-Making of Autonomous Vehicles to Predict Accidents

Abida Khanum, Chao Yang Lee, Chu Sing Yang

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

Abstract

The autonomous vehicle system ensures safe driving by certainly controlling a vehicle's motion in the lane to reduce the risk of a collision. The proposed architecture is based on convolutional neural network (CNN) deep learning-based with three convolutional blocks. As output, the CNN framework can predict three different risk factor evaluations, i.e., high risk, medium risk, and low risk, used to predict the accident of the AV. The deep learning-based network had a better accuracy rate and computing time for all MTs with a 70% accuracy rate and an 8-ms computing time for risk prediction in autonomous vehicles. The experimental accuracy demonstrates that our trained method has fruitfully accurate results. The self-driving vehicle model successfully predicts the accident risk during driving a vehicle on the road.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages90-92
Number of pages3
ISBN (Electronic)9798350313154
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023 - Brisbane, Australia
Duration: 2023 Jul 102023 Jul 14

Publication series

NameProceedings - 2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

Conference

Conference2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023
Country/TerritoryAustralia
CityBrisbane
Period23-07-1023-07-14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Media Technology
  • Control and Optimization

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