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Electronics
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Electronic Architecture for Autonomous Vehicles
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Electronic Architecture for Autonomous Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 4548

Editors

Dr. Felipe Jiménez

grade E-Mail Website
Guest Editor
Instituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: autonomous vehicles; cooperative services; assistance systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jose Eugenio Naranjo

E-Mail Website
Guest Editor
Instituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: autonomous vehicles; cooperative services; vehicular communications
Special Issues, Collections and Topics in MDPI journals
Dr. Alfredo Valle

E-Mail Website
Guest Editor
Instituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: autonomous vehicles; path planning; vehicular electronics

Special Issue Information

Dear Colleagues,

The introduction of vehicles with a higher level of automation is proving to be a challenge, especially when dealing with complex scenarios in which sensory perception does not allow for complete and robust situational awareness, but in which the system must be able to make decisions. All of this is leading to a change in the electronic architecture, enabling the merging of information from different sensors and its processing in control units to offer more complete decision-making solutions. Therefore, it is crucial to study the design of this architecture in order to offer sufficient flexibility for the advances that are being introduced at the application level. This architecture includes the organization of sensors and control units. Aspects that guarantee the security of the system as a whole and its robustness must also be considered, as well as the relationship between the electronic architecture and embedded software, since the latter must be adapted to perform calculations in the most efficient way possible on large volumes of information that may be incomplete or not totally reliable. Additionally, in addition to road vehicles, circulating on highways or urban environments, sectors such as the military, agriculture, construction, etc., may require specific architectures that optimize performance. Finally, the architecture must be open to different control strategies such as automated guidance, remote guidance, or remote monitoring, as well as to information sources that convey data from the vehicle's own sensors to wireless communication systems with the outside (other vehicles or infrastructure).

This Special Issue aims to collect articles related to the electronic and software architecture that must be implemented in highly automated vehicles so that they are capable of dealing with complex scenarios by processing the information obtained via on-board sensors and/or wireless communications. It also aims to present the variety of approaches that are currently being utilized, as well as the application of architectures oriented to certain sectors or employed in specific contexts. Finally, we welcome the submission of state-of-the-art studies on this architecture.

Dr. Felipe Jiménez
Prof. Dr. Jose Eugenio Naranjo
Dr. Alfredo Valle
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics 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

  • automated vehicles
  • electronic architecture
  • software
  • sensors
  • decision making
  • control unit

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Published Papers (5 papers)

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Research

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23 pages, 6900 KB  
Article
Can World Foundation Models Generate Realistic Driving Videos? A Case Study on Pedestrian Crossing Scenarios
by Cong Zhou, Qian Lu, Safraz Ahmed, Olivier Haas and Vasile Palade
Electronics 2026, 15(14), 3033; https://doi.org/10.3390/electronics15143033 - 10 Jul 2026
Viewed by 312
Abstract
Autonomous vehicle (AV) technologies have advanced rapidly in recent years, driving an increasing demand for large-scale, high-quality annotated data. However, collecting and annotating real-world pedestrian video datasets is time-consuming, costly, and often insufficient to cover rare and safety-critical scenarios. Recent world foundation models [...] Read more.
Autonomous vehicle (AV) technologies have advanced rapidly in recent years, driving an increasing demand for large-scale, high-quality annotated data. However, collecting and annotating real-world pedestrian video datasets is time-consuming, costly, and often insufficient to cover rare and safety-critical scenarios. Recent world foundation models have demonstrated impressive capabilities in generating realistic videos, yet their suitability for safety-critical autonomous driving applications remains largely unexplored. In this work, we investigate whether current world foundation models can generate driving scenarios that are sufficiently realistic and behaviourally consistent for autonomous driving research. We conduct a case study centred on pedestrian–vehicle interactions captured from ego-vehicle dashcam viewpoints, where subtle behavioural and geometric errors can have significant safety implications. To support this investigation, we develop SynPeDAS, an open research framework comprising a collection of synthetic pedestrian-interaction videos, a reusable generation pipeline for transforming real-world driving footage into synthetic scenarios, an automated evaluation suite, and downstream demonstration code. Through quantitative evaluation and structured human assessment, we identify several recurring failure modes, including dynamic misalignment, depth drift, and object persistence inconsistencies. More importantly, we find that commonly used evaluation metrics frequently exhibit ceiling effects and weak alignment with human judgement, limiting their ability to detect safety-critical behavioural errors. These findings indicate that, despite high perceptual realism at the frame level, current generative world models and existing evaluation methodologies remain insufficient for capturing physically grounded motion and task-critical semantics. Consequently, significant challenges remain before world model-generated videos can be considered reliable for safety-critical autonomous driving applications. SynPeDAS provides an open platform for systematically studying these challenges and developing improved generation and evaluation methods. Full article
(This article belongs to the Special Issue Electronic Architecture for Autonomous Vehicles)
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16 pages, 1353 KB  
Article
AI-Enabled Low-Level Signal Anomaly Detection in Virtualized Electronic Architectures for Autonomous Vehicles
by Mohsen Malayjerdi, Matin Afshari, Raivo Sell and Heiko Pikner
Electronics 2026, 15(12), 2515; https://doi.org/10.3390/electronics15122515 - 8 Jun 2026
Viewed by 287
Abstract
The safety of autonomous vehicles depends not only on perception and planning, but also on the correctness of low-level electronic signals that connect controllers and actuators. Errors at this interface, caused by hardware degradation, timing violations, software faults, or unexpected interactions, can lead [...] Read more.
The safety of autonomous vehicles depends not only on perception and planning, but also on the correctness of low-level electronic signals that connect controllers and actuators. Errors at this interface, caused by hardware degradation, timing violations, software faults, or unexpected interactions, can lead to unsafe behavior even when high-level autonomy functions operate correctly. Existing safety mechanisms primarily focus on system behavior, trajectories, or controller design, leaving actuator-bound command streams largely unmonitored. This paper proposes a low-level, AI-enabled anomaly-detection layer for autonomous vehicle architectures. The core idea is to embed a lightweight observer within a virtualized master controller to monitor control-signal streams in real time without interfering with the primary control logic. The proposed framework combines a stacked LSTM sequence classifier with rule-based safety constraints and context-aware monitoring to detect physically implausible or temporally inconsistent command behavior before actuation. A proof-of-concept simulation study was conducted to evaluate the practicality of the approach using overtaking scenarios in a co-simulated high-level and low-level environment. The results show that the proposed concept can identify severe abnormal low-level behavior and provide preliminary warning/error indications, supporting its potential as a complementary safety layer at the control-to-actuation interface. Full article
(This article belongs to the Special Issue Electronic Architecture for Autonomous Vehicles)
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33 pages, 11328 KB  
Article
Artificial Intelligence for Autonomous Vehicles: Robustness Analysis in Complex Urban Traffic Scenarios
by Brandon Quezada-Godoy, Antonio Guerrero-González, Francisco García-Córdova, Francisco Lloret-Abrisqueta and Antonio Jesús Martínez-Espinosa
Electronics 2026, 15(10), 2204; https://doi.org/10.3390/electronics15102204 - 20 May 2026
Viewed by 490
Abstract
Autonomous driving in complex urban environments remains challenging due to perception uncertainty, dynamic multi-agent interactions, and control instability under adverse conditions. Despite advances in individual components, systematic evaluations of fully integrated modular pipelines under compounded urban disturbances remain scarce. This work presents a [...] Read more.
Autonomous driving in complex urban environments remains challenging due to perception uncertainty, dynamic multi-agent interactions, and control instability under adverse conditions. Despite advances in individual components, systematic evaluations of fully integrated modular pipelines under compounded urban disturbances remain scarce. This work presents a modular autonomous driving framework in CARLA Town10HD, integrating Convolutional Neural Network (CNN)-based perception using ResNet-18, global path planning via A* algorithm, and two control strategies: a classical Proportional–Integral–Derivative (PID) controller and a Deep Q-Network (DQN) agent with adaptive geometric steering assistance. A structured protocol assessed robustness across five scenarios: Heavy Rain, Dense Fog, Nighttime Driving, Dense Traffic, and Combined Extreme Conditions. The perception module achieved F1-scores close to 0.99 for traffic-sign, pedestrian, and lane classification; results reflect synthetic CARLA data and should not be interpreted as real-world generalization. The PID controller produced smoother trajectories with lower steering oscillations, while the DQN agent achieved faster traversal times at the cost of higher control variability. Route efficiency remained around 0.96 under isolated disturbances and decreased to 0.52 under compounded conditions, confirming sensitivity to multi-factor complexity. This study contributes a reproducible multi-scenario benchmark quantifying stability–adaptability trade-offs between classical and learning-based control, identifying scenario generalization and simulation-to-reality transfer as key future directions. Full article
(This article belongs to the Special Issue Electronic Architecture for Autonomous Vehicles)
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32 pages, 3454 KB  
Article
Research on Advancement Constraint Screening and Cost Evaluation of Centralized Architecture Platforms for Intelligent Vehicles Under Different R&D Solutions
by Wang Zhang, Fuquan Zhao and Zongwei Liu
Electronics 2026, 15(8), 1605; https://doi.org/10.3390/electronics15081605 - 12 Apr 2026
Cited by 1 | Viewed by 685
Abstract
The electronic and electrical architecture of vehicles has rapidly evolved to centralized. At present, there is no unified consensus on the R&D strategy of the platform in the industry, and there is also a lack of a quantitative decision-making framework that can be [...] Read more.
The electronic and electrical architecture of vehicles has rapidly evolved to centralized. At present, there is no unified consensus on the R&D strategy of the platform in the industry, and there is also a lack of a quantitative decision-making framework that can be implemented. This study takes the centralized architecture platform as the research object, constructs a two-stage analysis framework of “advanced constraint screening-cost quantitative evaluation”, uses a fuzzy-set qualitative comparative analysis method to screen feasible R&D strategy combinations that meet the requirements of the architectural advancement, builds a total cost of ownership evaluation system around the software and hardware elements related to the architecture platform, and systematically analyzes the optimal cost R&D strategy combinations of car enterprises with different mass production scales under the two scenarios of Multi-Box and One-Board. The research results show that adaptive platform middleware and framework middleware are the core necessary elements to realize the advanced architecture; the amortization cost of architecture is negatively correlated with the scale of mass production, and the cost of in-house R&D is highly dependent on large-scale amortization; and there are differentiated optimal solutions in the framework selection and R&D strategy combination of automakers with different mass production scales. This study can provide quantitative reference and practical guidance for R&D decision making of centralized architecture platform for automakers. Full article
(This article belongs to the Special Issue Electronic Architecture for Autonomous Vehicles)
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31 pages, 643 KB  
Systematic Review
The Use of Business Intelligence and Analytics in Electric Vehicle Technology: A Comprehensive Survey
by Alexandra Bousia
Electronics 2026, 15(2), 366; https://doi.org/10.3390/electronics15020366 - 14 Jan 2026
Cited by 2 | Viewed by 1710
Abstract
The emerging urbanization and the extensive increase of the transportation sector are responsible for the significant increase in carbon dioxide emissions. Therefore, replacing traditional cars with Electric Vehicles (EVs) is a promising solution, offering a clearer alternative. EVs are becoming more and more [...] Read more.
The emerging urbanization and the extensive increase of the transportation sector are responsible for the significant increase in carbon dioxide emissions. Therefore, replacing traditional cars with Electric Vehicles (EVs) is a promising solution, offering a clearer alternative. EVs are becoming more and more well-known and are being quickly used worldwide. However, the exponential rise in EV sales has also raised a number of issues, which are becoming important and demanding. These challenges include the need of driving security, the battery degradation, the inadequate infrastructure for charging EVs, and the uneven energy distribution. In order for EVs to reach their full potential, intelligent systems and innovative technologies need to be introduced in the field of EVs. This is where business intelligence (BI) can be employed, along with artificial intelligence (AI), data analytics, and machine learning. In this paper, we provide a comprehensive survey on the use of BI strategies in the EV transportation sector. We first introduce the EVs and charging station technologies. Then, research works on the application of BI and data analysis techniques in EV technology are reviewed to further understand the challenges and open issues for the research and industry community. Moreover, related works on accident analysis, battery health prediction, charging station analysis, intelligent infrastructure, locating charging stations analysis, and autonomous driving are investigated. This survey systematically reviews 75 peer-reviewed studies published between 2020 and 2025. Finally, we discuss the fundamental limitations and the future open challenges in the aforementioned topics. Full article
(This article belongs to the Special Issue Electronic Architecture for Autonomous Vehicles)
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