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

Abida Khanum, Chao Yang Lee, Chu Sing Yang

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Proceedings - 2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023
發行者Institute of Electrical and Electronics Engineers Inc.
頁面90-92
頁數3
ISBN(電子)9798350313154
DOIs
出版狀態Published - 2023
事件2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023 - Brisbane, Australia
持續時間: 2023 7月 102023 7月 14

出版系列

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

Conference

Conference2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023
國家/地區Australia
城市Brisbane
期間23-07-1023-07-14

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦科學應用
  • 電腦視覺和模式識別
  • 媒體技術
  • 控制和優化

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