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Algorithms
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Machine Learning Algorithms for Distributed Autonomous Vehicles
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Machine Learning Algorithms for Distributed Autonomous Vehicles

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2819

Special Issue Editor

Dr. Dadmehr Rahbari

E-Mail Website
Guest Editor
Department of Computer Systems, School of Information Technologies, Tallinn University of Technology, 12618 Tallinn, Estonia
Interests: resource management; security; distributed computing; machine learning

Special Issue Information

Dear Colleagues,

Autonomous vehicles are growing in popularity, with many new applications emerging in different environments and architectures, e.g., cloud/edge/fog computing. The increasing attention and need for these systems are forcing us to take a deeper look into the challenges associated with them. Resource management, i.e., appropriate allocation of resources to tasks, is one of the challenges of this type of system. Additionally, since these devices are heterogeneous, i.e., they have the different processing and transferring power, memory, sensors, bandwidth, batteries, etc., some may not be able to respond in the necessary time and with the appropriate energy expenditure and have to offload part of their computation tasks to other devices. This cooperation involves a range of challenges, such as trust and reliability, which should be considered in resource management strategies. Through machine learning algorithms, it is hoped that these systems will act intelligently and autonomously to a significant extent.

We welcome innovative contributions on optimization algorithms for autonomous vehicles. Topics include but are not limited to:

  • Cloud/edge/fog computing;
  • Autonomous vehicles;
  • Resource management;
  • Task scheduling;
  • Computation offloading;
  • Trusted and reliable cooperation;
  • Machine learning;
  • Resource allocation.

Dr. Dadmehr Rahbari
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. Algorithms 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 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

  • cloud/edge/fog computing
  • autonomous vehicles
  • resource management
  • task scheduling
  • computation offloading
  • trusted and reliable cooperation
  • machine learning
  • resource allocation

Published Papers (2 papers)

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Research

19 pages, 4791 KiB  
Article
A Heterogeneity-Aware Car-Following Model: Based on the XGBoost Method
by Kefei Zhu, Xu Yang, Yanbo Zhang, Mengkun Liang and Jun Wu
Algorithms 2024, 17(2), 68; https://doi.org/10.3390/a17020068 - 5 Feb 2024
Viewed by 1196
Abstract
With the rising popularity of the Advanced Driver Assistance System (ADAS), there is an increasing demand for more human-like car-following performance. In this paper, we consider the role of heterogeneity in car-following behavior within car-following modeling. We incorporate car-following heterogeneity factors into the [...] Read more.
With the rising popularity of the Advanced Driver Assistance System (ADAS), there is an increasing demand for more human-like car-following performance. In this paper, we consider the role of heterogeneity in car-following behavior within car-following modeling. We incorporate car-following heterogeneity factors into the model features. We employ the eXtreme Gradient Boosting (XGBoost) method to build the car-following model. The results show that our model achieves optimal performance with a mean squared error of 0.002181, surpassing the model that disregards heterogeneity factors. Furthermore, utilizing model importance analysis, we determined that the cumulative importance score of heterogeneity factors in the model is 0.7262. The results demonstrate the significant impact of heterogeneity factors on car-following behavior prediction and highlight the importance of incorporating heterogeneity factors into car-following models. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Distributed Autonomous Vehicles)
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21 pages, 4436 KiB  
Article
Generation of Achievable Three-Dimensional Trajectories for Autonomous Wheeled Vehicles via Tracking Differentiators
by Svetlana A. Krasnova, Julia G. Kokunko, Sergey A. Kochetkov and Victor A. Utkin
Algorithms 2023, 16(9), 405; https://doi.org/10.3390/a16090405 - 25 Aug 2023
Viewed by 772
Abstract
Planning an achievable trajectory for a mobile robot usually consists of two steps: (i) finding a path in the form of a sequence of discrete waypoints and (ii) transforming this sequence into a continuous and smooth curve. To solve the second problem, this [...] Read more.
Planning an achievable trajectory for a mobile robot usually consists of two steps: (i) finding a path in the form of a sequence of discrete waypoints and (ii) transforming this sequence into a continuous and smooth curve. To solve the second problem, this paper proposes algorithms for automatic dynamic smoothing of the primary path using a tracking differentiator with sigmoid corrective actions. Algorithms for setting the gains of the differentiator are developed, considering a set of design constraints on velocity, acceleration, and jerk for various mobile robots. When tracking a non-smooth primary path, the output variables of the differentiator generate smooth trajectories implemented by a mechanical plant. It is shown that the tracking differentiator with a different number of blocks also generates derivatives of the smoothed trajectory of any required order, taking into account the given constraints. Unlike standard analytical methods of polynomial smoothing, the proposed algorithm has a low computational load. It is easily implemented in real time on the on-board computer. In addition, simple methods for modeling a safety corridor are proposed, taking into account the dimensions of the vehicle when planning a polygon with stationary obstacles. Confirming results of numerical simulation of the developed algorithms are presented. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Distributed Autonomous Vehicles)
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