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Machines
Special Issues
Dynamics and Control of Autonomous Vehicles
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Dynamics and Control of Autonomous Vehicles

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 2830

Special Issue Editors

Dr. Hormoz Marzbani

E-Mail Website
Guest Editor
Royal Melbourne Institute of Technology, School of Engineering, Bundoora, VIC 3083, Australia
Interests: vehicle dynamics and control; autonomous land vehicles
Prof. Dr. Reza Jazar

E-Mail Website
Guest Editor
Royal Melbourne Institute of Technology, School of Engineering, Bundoora, VIC 3083, Australia
Interests: nonlinear dynamics and vibration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rather complex problem of autonomous vehicles control has captured the attention of multi-disciplinary researchers for many years now. Although computer science initially showed promise in solving this problem, we now know that the nonlinearities and complexities involved in critical scenarios of the problem requires dynamics scientists and experts to address the remaining issues, especially considering the safety, comfort and convenience of passengers, as well as the variable environmental factors and highly stochastic fellow agents, such as other vehicles and pedestrians. This Special Issue aims to bring researchers together to present recent advances and technologies in the field of dynamics and control, passenger-centered control, and safety critical control of autonomous vehicles while addressing uncertainty in autonomous vehicle control. Topics include, but are not limited to, the following:

  • New modelling and control methods for autonomous vehicles;
  • Time and safety critical control of autonomous vehicles for maximum safety;
  • Comfort-focused control strategies for autonomous vehicles;
  • Autonomous vehicle control at the handling limits;
  • Uncertainties in the control of autonomous vehicles.

Dr. Hormoz Marzbani
Prof. Dr. Reza Jazar
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. Machines is an international peer-reviewed open access monthly 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 2400 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

  • autonomous land vehicles
  • vehicle dynamics
  • vehicle control and handling
  • uncertainty in autonomous vehicles
  • safety in autonomous vehicles

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Published Papers (1 paper)

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Research

17 pages, 1566 KiB  
Article
View-Invariant Spatiotemporal Attentive Motion Planning and Control Network for Autonomous Vehicles
by Melese Ayalew, Shijie Zhou, Imran Memon, Md Belal Bin Heyat, Faijan Akhtar and Xiaojuan Zhang
Machines 2022, 10(12), 1193; https://doi.org/10.3390/machines10121193 - 9 Dec 2022
Cited by 2 | Viewed by 2205
Abstract
Autonomous driving vehicles (ADVs) are sleeping giant intelligent machines that perceive their environment and make driving decisions. Most existing ADSs are built as hand-engineered perception-planning-control pipelines. However, designing generalized handcrafted rules for autonomous driving in an urban environment is complex. An alternative approach [...] Read more.
Autonomous driving vehicles (ADVs) are sleeping giant intelligent machines that perceive their environment and make driving decisions. Most existing ADSs are built as hand-engineered perception-planning-control pipelines. However, designing generalized handcrafted rules for autonomous driving in an urban environment is complex. An alternative approach is imitation learning (IL) from human driving demonstrations. However, most previous studies on IL for autonomous driving face several critical challenges: (1) poor generalization ability toward the unseen environment due to distribution shift problems such as changes in driving views and weather conditions; (2) lack of interpretability; and (3) mostly trained to learn the single driving task. To address these challenges, we propose a view-invariant spatiotemporal attentive planning and control network for autonomous vehicles. The proposed method first extracts spatiotemporal representations from images of a front and top driving view sequence through attentive Siamese 3DResNet. Then, the maximum mean discrepancy loss (MMD) is employed to minimize spatiotemporal discrepancies between these driving views and produce an invariant spatiotemporal representation, which reduces domain shift due to view change. Finally, the multitasking learning (MTL) method is employed to jointly train trajectory planning and high-level control tasks based on learned representations and previous motions. Results of extensive experimental evaluations on a large autonomous driving dataset with various weather/lighting conditions verified that the proposed method is effective for feasible motion planning and control in autonomous vehicles. Full article
(This article belongs to the Special Issue Dynamics and Control of Autonomous Vehicles)
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