Predicting Steering for Autonomous Vehicles Based on Crowd Sensing and Deep Learning

The challenge in ensuring the reliability of autonomous vehicles is full awareness of the surrounding environment and high-precision steering control. The latest solutions to this challenge include deep learning technologies that provide end-to-end solutions to predict steering angles directly from environmental cognition information with high accuracy. Under the background of 5G technology, edge device has certain computing power, which can reduce the load of on-board computing equipment. In this paper, the authors present a new distributed perception-decision network model. This model allows the network’s computing tasks to be offloaded to the edge computing devices to reduce the consumption of vehicle-mounted computing devices. The feasibility of the model is verified by experiments. Compared with the existing methods, the model also has a higher accuracy of steering prediction.

• Record URL:
  • https://doi.org/10.1007/978-3-030-38651-1
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9783030386504
• Supplemental Notes:
  • © Springer Nature Switzerland AG 2020.
• Corporate Authors:

  Springer International Publishing

  ,    
• Authors:
  • Liu, Taiyu
  • Li, Jinglin
  • Yuan, Quan
• Conference:
  • 6th International Conference on Internet of Vehicles (IOV 2019)
  • Location: Kaohsiung , Taiwan
  • Date: 2019-11-18 to 2019-11-21
• Publication Date: 2020-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 117-124
• Monograph Title: Internet of Vehicles. Technologies and Services Toward Smart Cities: 6th International Conference, IOV 2019, Kaohsiung, Taiwan, November 18–21, 2019, Proceedings
• Serial:
  • Lecture Notes in Computer Science
  • Volume: 11894
  • Publisher: Springer Cham
  • ISSN: 0302-9743
  • Serial URL: https://www.springer.com/series/558

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Crowdsourcing; Decision making; Machine learning; Perception; Steering
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01892936
• Record Type: Publication
• ISBN: 9783030386504
• Files: TRIS
• Created Date: Sep 12 2023 9:20AM