Special Issue "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: 31 December 2023 | Viewed by 927

Special Issue 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

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

Published Papers (1 paper)

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Generation of Achievable Three-Dimensional Trajectories for Autonomous Wheeled Vehicles via Tracking Differentiators
Algorithms 2023, 16(9), 405; https://doi.org/10.3390/a16090405 - 25 Aug 2023
Viewed by 343
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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