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Vehicles
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Autonomous Vehicle Control
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Autonomous Vehicle Control

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 13347

Special Issue Editor

Dr. Sohel Anwar

E-Mail Website
Guest Editor
Department of Mechanical and Energy Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
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.

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. Vehicles is an international peer-reviewed open access quarterly 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 1600 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 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

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Published Papers (3 papers)

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Research

19 pages, 3722 KiB  
Article
Intersection Control and Delay Optimization for Autonomous Vehicles Flows Only as Well as Mixed Flows with Ordinary Vehicles
by Abdullah Baz, Ping Yi and Ahmad Qurashi
Vehicles 2020, 2(3), 523-541; https://doi.org/10.3390/vehicles2030029 - 26 Aug 2020
Cited by 9 | Viewed by 4299
Abstract
The rapidly improving autonomous vehicle (AV) technology will have a significant impact on traffic safety and efficiency. This study introduces a game-theory-based priority control algorithm for autonomous vehicles to improve intersection safety and efficiency with mixed traffic. By using vehicle-to-infrastructure (V2I) communications, this [...] Read more.
The rapidly improving autonomous vehicle (AV) technology will have a significant impact on traffic safety and efficiency. This study introduces a game-theory-based priority control algorithm for autonomous vehicles to improve intersection safety and efficiency with mixed traffic. By using vehicle-to-infrastructure (V2I) communications, this model allows an AV to exchange information with the roadside units (RSU) to support the decision making of whether an ordinary vehicle (OV) or an AV should pass the intersection first. The safety of vehicles is taken in different stages of decisions to assure collision-free intersection operations. Two different mathematical models have been developed, where model one is for an AV/AV situation and model two is when an AV meets an OV. A simulation model was developed to implement the algorithm and compare the performance of each model with the conventional traffic control at a four-legged signalized intersection and at a roundabout. Three levels of traffic volume and speed combinations were tested in the simulation. The results show significant reductions in delay for both cases; for case (I), AV/AV model, a 65% reduction compared to a roundabout and 84% compared to a four-legged signalized intersection, and for case (II), AV/OV model, the reduction is 30% and 89%, respectively. Full article
(This article belongs to the Special Issue Autonomous Vehicle Control)
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23 pages, 1702 KiB  
Article
Adaptive Neural Motion Control of a Quadrotor UAV
by Hugo Yañez-Badillo, Ruben Tapia-Olvera and Francisco Beltran-Carbajal
Vehicles 2020, 2(3), 468-490; https://doi.org/10.3390/vehicles2030026 - 20 Jul 2020
Cited by 7 | Viewed by 3445
Abstract
Unmanned Aerial Vehicles have generated considerable interest in different research fields. The motion control problem is among the most important issues to be solved since system dynamic stability depends on the robustness of the main controller against endogenous and exogenous disturbances. In spite [...] Read more.
Unmanned Aerial Vehicles have generated considerable interest in different research fields. The motion control problem is among the most important issues to be solved since system dynamic stability depends on the robustness of the main controller against endogenous and exogenous disturbances. In spite of different controllers have been introduced in the literature for motion control of fixed and rotary wing vehicles, there are some challenges for improving controller features such as simplicity, robustness, efficiency, adaptability, and stability. This paper outlines a novel approach to deal with the induced effects of external disturbances affecting the flight of a quadrotor unmanned aerial vehicle. The aim of our study is to further extend the current knowledge of quadrotor motion control by using both adaptive and robust control strategies. A new adaptive neural trajectory tracking control strategy based on B-spline artificial neural networks and on-line disturbance estimation for a quadrotor is proposed. A linear extended state observer is used for estimating time-varying disturbances affecting the controlled nonlinear system dynamics. B-spline artificial neural networks are properly synthesized for on-line calculating control gains of an adaptive Proportional Integral Derivative (PID) scheme. Simulation results highlight the implementation of such a controller is able to reject disturbances meanwhile perform proper motion control by exploiting the robustness, disturbance rejection, adaptability, and self-learning capabilities. Full article
(This article belongs to the Special Issue Autonomous Vehicle Control)
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Graphical abstract

18 pages, 2553 KiB  
Article
Cooperative Highway Lane Merge of Connected Vehicles Using Nonlinear Model Predictive Optimal Controller
by Syed A. Hussain, Babak Shahian Jahromi and Sabri Cetin
Vehicles 2020, 2(2), 249-266; https://doi.org/10.3390/vehicles2020014 - 25 Mar 2020
Cited by 12 | Viewed by 4176
Abstract
Of all driving functions, one of the critical maneuvers is the lane merge. A cooperative Nonlinear Model Predictive Control (NMPC)-based optimization method for implementing a highway lane merge of two connected autonomous vehicles is presented using solutions obtained by the direct multiple shooting [...] Read more.
Of all driving functions, one of the critical maneuvers is the lane merge. A cooperative Nonlinear Model Predictive Control (NMPC)-based optimization method for implementing a highway lane merge of two connected autonomous vehicles is presented using solutions obtained by the direct multiple shooting method. A performance criteria cost function, which is a function of the states and inputs of the system, was optimized subject to nonlinear model and maneuver constraints. An optimal formulation was developed and then solved on a receding horizon using direct multiple shooting solutions; this is implemented using an open-source ACADO code. Numerical simulation results were performed in a real-case scenario. The results indicate that the implementation of such a controller is possible in real time, in different highway merge situations. Full article
(This article belongs to the Special Issue Autonomous Vehicle Control)
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