Deep learning for autonomous vehicle and pedestrian interaction safety

The present work aims to study how deep learning approaches solve the safety problems in the interaction between autonomous vehicles and pedestrians. A Vehicle-Pedestrian Detection (VPD) algorithm based on Convolutional Neural Network (CNN) is proposed regarding the massive amounts of parameters during feature extraction of traditional vehicle–pedestrian interaction algorithms. Furthermore, the Squeezenet algorithm is applied to extract traffic characteristics with fewer parameters. The performance of the proposed algorithm is analyzed through simulation experiments. Results demonstrate that when the successful transmission probability reaches 100% and the λ value is 0.01–0.05, the proposed algorithm can provide the result closest to the actual value, with the smallest data delay and the highest data transmission security. In different categories, the proposed algorithm can provide the highest accuracy as the number of iterations increases compared with other algorithms (AlexNet, DenseNet, VGGNet, IGCNet, and ResNet), which can accurately forecast traffic safety accidents. The proposed algorithm can provide an accuracy of 81.98%, an improvement of at least 1.94% over other advanced CNNs, and at least 3.3% over algorithms included in comparative simulations. Hence, it can recognize and identify safe interactions between autonomous vehicles and pedestrians. Through experiments, the constructed algorithm can significantly reduce the data transmission delay, improve the prediction accuracy of the safe interaction between autonomous vehicles and pedestrians, and increase the recognition accuracy remarkably, which can provide experimental references for the intelligent development of the transportation industry in the future.

• Record URL:
  https://doi.org/10.1016/j.ssci.2021.105479
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0925753521003222
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09257535
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhu, Zijiang
  • Hu, Zhenlong
  • Dai, Weihuang
  • Chen, Hang
  • Lv, Zhihan
• Publication Date: 2022-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 105479
• Serial:
  • Safety Science
  • Volume: 145
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0925-7535
  • Serial URL: http://www.sciencedirect.com/science/journal/09257535

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Crash risk forecasting; Machine learning; Pedestrian safety; Pedestrian vehicle crashes; Pedestrian vehicle interface
• Subject Areas: Highways; Pedestrians and Bicyclists; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01783305
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 28 2021 11:30AM