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Advanced AI Technologies for Positioning and Perception in Autonomous Electric Vehicles

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1042

Special Issue Editors


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Guest Editor
Key Laboratory of Bionic Engineering (Ministry of Education), Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China
Interests: electric vehicles; vehicle dynamics; autonomous driving; robotics

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Guest Editor
Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: autonomous driving, electric vehicles and intelligent systems; new generation clean propulsion control and optimisation; digital modelling and simulation; intelligent transportation system and artificial intelligence (AI) in engineering practice
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of artificial intelligence (AI) is transforming the landscape of electric vehicles (EVs) and autonomous driving, particularly in the areas of positioning and perception. This Special Issue aims to gather recent research and innovations that leverage AI to enhance the accuracy, reliability, and efficiency of positioning and perception systems in autonomous electric vehicles.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • AI-based algorithms for precise positioning in autonomous EVs;
  • Machine learning and deep learning approaches for vehicle perception;
  • Sensor fusion techniques enhanced by AI for improved situational awareness;
  • AI-driven object detection and classification for autonomous driving;
  • Real-time processing and decision making using AI in EVs;
  • Autonomous navigation systems integrating AI for better positioning accuracy;
  • Impact of AI on the energy efficiency and performance of autonomous electric vehicles;
  • Safety and reliability improvements in autonomous driving through AI-based perception systems;
  • Case studies and simulations of AI-enhanced positioning and perception in EVs;
  • Future trends and challenges in AI applications for autonomous driving and electric vehicles.

Dr. Di Zhao
Dr. Yuanjian Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent vehicles
  • positioning and navigation
  • autonomous driving
  • road–vehicle cooperative control
  • sensing or computer vision
  • AI

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Published Papers (1 paper)

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Research

15 pages, 4684 KiB  
Article
Online Calibration of Inertial Sensors Based on Error Backpropagation
by Vojtech Simak, Jan Andel, Dusan Nemec and Juraj Kekelak
Sensors 2024, 24(23), 7525; https://doi.org/10.3390/s24237525 - 25 Nov 2024
Viewed by 784
Abstract
Global satellite navigation systems (GNSSs) are the most-used technology for the localization of vehicles in the outdoor environment, but in the case of a densely built-up area or during passage through a tunnel, the satellite signal is not available or has poor quality. [...] Read more.
Global satellite navigation systems (GNSSs) are the most-used technology for the localization of vehicles in the outdoor environment, but in the case of a densely built-up area or during passage through a tunnel, the satellite signal is not available or has poor quality. Inertial navigation systems (INSs) allow localization dead reckoning, but they have an integration error that grows over time. Inexpensive inertial measurement units (IMUs) are subject to thermal-dependent error and must be calibrated almost continuously. This article proposes a novel method of online (continuous) calibration of inertial sensors with the aid of the data from the GNSS receiver during the vehicle’s route. We performed data fusion using an extended Kalman filter (EKF) and calibrated the input sensors through error backpropagation. The algorithm thus calibrates the INS sensors while the GNSS receiver signal is good, and after a GNSS failure, for example in tunnels, the position is predicted only by low-cost inertial sensors. Such an approach significantly improved the localization precision in comparison with offline calibrated inertial localization with the same sensors. Full article
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