Autonomous Vehicle Control
A special issue of Vehicles (ISSN 2624-8921).
Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 10951
Special Issue Editor
Interests: novel sensor development and data fusion; advanced diagnostics/management of traction batteries; power/energy management of electrified powertrains; autonomous vehicle control and X-by-wire systems; biomechanical device design and control; wind turbine modeling and control
Special Issue Information
Dear colleagues,
Autonomous vehicle technologies are advancing at a faster pace in recent years. There have been many exciting developments in new technologies that may contribute to improving the robustness of autonomous driving systems and thus making autonomous vehicles safer on the road. We are looking for original contributions in this Special Issue of Vehicles titled “Autonomous Vehicle Control”. Topics include but are not limited to sensing/sensor fusion for object detection, autonomous navigation, autonomous vehicle modeling and simulation, the SLAM algorithm, and machine learning/deep learning-based robust detection algorithms.
Prof. Dr. Sohel Anwar
Guest Editor
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.
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Keywords
- autonomous vehicle systems
- novel sensing systems for object detection
- sensor fusion algorithm
- SLAM
- robust vision-based detection
- autonomous vehicle simulation testing
- GPS guided navigation
- deep learning/machine learning-based detection