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Applied Sciences
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Design and Applications of Real-Time Embedded Systems
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Design and Applications of Real-Time Embedded Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 1603

Special Issue Editor

Dr. Jalil Boudjadar

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Aarhus University, 8200 Aarhus, Denmark
Interests: real-time embedded systems; digital twins; formal methods; embodied AI & adaptive systems; SW & HW architectures

Special Issue Information

Dear Colleagues,

Real-time embedded systems are crucial bridges between the physical and the information worlds in the ever-growing embedded intelligence pervasiveness in industry and infrastructure.

The objective of this Special Issue is to invite original and high-quality papers that describe research, technical aspects, or developments in real-time embedded systems design and applications. Contributions in industrial automation and control, energy management, automotive, aerospace and defense systems, as well as in emerging domains, such as in household appliances, mobile multimedia, and gaming systems, are welcome.

Topics of interest include, but are not limited to, the following areas:

  • Applications of real-time embedded systems: design and performance evaluation.
  • Security, reliability, and fault tolerance for real-time embedded systems.
  • Machine learning and edge computing for embedded systems.
  • Scheduling for real-time embedded systems.
  • Testing, verification, and validation of real-time embedded systems.
  • Formal methods, timing analysis, scheduling design, analysis and verification.
  • Real-time embedded systems design.
  • Networked embedded systems.
  • Real-time networks, network management and time synchronization.

Dr. Jalil Boudjadar
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • embedded systems
  • real-time systems
  • intelligent and smart architectures
  • design and analysis methods and tools
  • networked embedded systems
  • machine learning

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Published Papers (2 papers)

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Review

25 pages, 4810 KB  
Review
Deep Reinforcement and IL for Autonomous Driving: A Review in the CARLA Simulation Environment
by Piotr Czechowski, Bartosz Kawa, Mustafa Sakhai and Maciej Wielgosz
Appl. Sci. 2025, 15(16), 8972; https://doi.org/10.3390/app15168972 - 14 Aug 2025
Viewed by 787
Abstract
Autonomous driving is a complex and fast-evolving domain at the intersection of robotics, machine learning, and control systems. This paper provides a systematic review of recent developments in reinforcement learning (RL) and imitation learning (IL) approaches for autonomous vehicle control, with a dedicated [...] Read more.
Autonomous driving is a complex and fast-evolving domain at the intersection of robotics, machine learning, and control systems. This paper provides a systematic review of recent developments in reinforcement learning (RL) and imitation learning (IL) approaches for autonomous vehicle control, with a dedicated focus on the CARLA simulator, an open-source, high-fidelity platform that has become a standard for learning-based autonomous vehicle (AV) research. We analyze RL-based and IL-based studies, extracting and comparing their formulations of state, action, and reward spaces. Special attention is given to the design of reward functions, control architectures, and integration pipelines. Comparative graphs and diagrams illustrate performance trade-offs. We further highlight gaps in generalization to real-world driving scenarios, robustness under dynamic environments, and scalability of agent architectures. Despite rapid progress, existing autonomous driving systems exhibit significant limitations. For instance, studies show that end-to-end reinforcement learning (RL) models can suffer from performance degradation of up to 35% when exposed to unseen weather or town conditions, and imitation learning (IL) agents trained solely on expert demonstrations exhibit up to 40% higher collision rates in novel environments. Furthermore, reward misspecification remains a critical issue—over 20% of reported failures in simulated environments stem from poorly calibrated reward signals. Generalization gaps, especially in RL, also manifest in task-specific overfitting, with agents failing up to 60% of the time when faced with dynamic obstacles not encountered during training. These persistent shortcomings underscore the need for more robust and sample-efficient learning strategies. Finally, we discuss hybrid paradigms that integrate IL and RL, such as Generative Adversarial IL, and propose future research directions. Full article
(This article belongs to the Special Issue Design and Applications of Real-Time Embedded Systems)
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21 pages, 1573 KB  
Review
A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
by Juliano Pimentel, Alistair A. McEwan and Hong Qing Yu
Appl. Sci. 2025, 15(15), 8538; https://doi.org/10.3390/app15158538 - 31 Jul 2025
Viewed by 454
Abstract
This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered [...] Read more.
This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered via exponentially weighted moving averages (EWMAs) and refined through SHAP-based feature attribution. Compared against a Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) across ten diverse drive cycles, the proposed model consistently achieved superior performance, with mean absolute errors (MAEs) as low as 0.40%, outperforming EKF (0.66%) and UKF (1.36%). The Bi-LSTM model also demonstrated higher R2 values (up to 0.9999) and narrower 95% confidence intervals, confirming its precision and robustness. Real-time implementation on embedded platforms yielded inference times of 1.3–2.2 s, validating its deployability for edge applications. The framework’s model-free nature makes it adaptable to other nonlinear, time-dependent systems beyond battery SOC estimation. Full article
(This article belongs to the Special Issue Design and Applications of Real-Time Embedded Systems)
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