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Sensors
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AI-Driven Sensor Technologies for Next-Generation Electric Vehicles
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AI-Driven Sensor Technologies for Next-Generation Electric Vehicles

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 219

Special Issue Editor

Prof. Dr. Cheng Siong Chin

E-Mail Website
Guest Editor
Intelligent Systems Design, Newcastle University, Singapore 038986, Singapore
Interests: intelligent systems design of complex systems in uncertain environments (underwater/electric vehicle, battery, PV system, acoustic enclosure, and water distribution network) involving predictive analytics (data mining, predictive modeling, and machine learning)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of electric vehicles (EVs) is driven by innovations in sensor technologies, which are essential for enhancing performance, safety, and sustainability. This Special Issue, entitled "AI-Driven Sensor Technologies for Next-Generation Electric Vehicles", welcomes the submission of cutting-edge research and reviews focused on the development, integration, and application of sensors in EV systems. Key topics of interest include battery management sensors (e.g., state-of-charge estimation, thermal monitoring), motor control, environmental perception (LiDAR, radar, cameras), autonomous driving, and energy efficiency optimization. Contributions are also welcome to explore novel sensor materials, fault detection algorithms, vehicle-to-everything (V2X) communication, and cybersecurity solutions tailored for EV ecosystems. Additionally, we welcome studies focused on multi-sensor fusion, real-time data processing, and the role of artificial intelligence (AI) and machine learning (ML) in improving sensor accuracy, reliability, and decision-making. This Special Issue aims to bridge the gap between academia and industry, fostering innovative sensor technologies that will shape the future of EVs. Interdisciplinary research addressing scalability, cost-effectiveness, and technical challenges is particularly encouraged.

Prof. Dr. Cheng Siong Chin
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. Sensors 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 2600 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

  • electric vehicle sensors
  • battery management systems
  • autonomous driving
  • multi-sensor fusion
  • AI/ML in EV sensors
  • cybersecurity for EVs
  • V2X communication

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Published Papers (1 paper)

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Research

22 pages, 6556 KiB  
Article
Multi-Task Trajectory Prediction Using a Vehicle-Lane Disentangled Conditional Variational Autoencoder
by Haoyang Chen, Na Li, Hangguan Shan, Eryun Liu and Zhiyu Xiang
Sensors 2025, 25(14), 4505; https://doi.org/10.3390/s25144505 - 20 Jul 2025
Abstract
Trajectory prediction under multimodal information is critical for autonomous driving, necessitating the integration of dynamic vehicle states and static high-definition (HD) maps to model complex agent–scene interactions effectively. However, existing methods often employ static scene encodings and unstructured latent spaces, limiting their ability [...] Read more.
Trajectory prediction under multimodal information is critical for autonomous driving, necessitating the integration of dynamic vehicle states and static high-definition (HD) maps to model complex agent–scene interactions effectively. However, existing methods often employ static scene encodings and unstructured latent spaces, limiting their ability to capture evolving spatial contexts and produce diverse yet contextually coherent predictions. To tackle these challenges, we propose MS-SLV, a novel generative framework that introduces (1) a time-aware scene encoder that aligns HD map features with vehicle motion to capture evolving scene semantics and (2) a structured latent model that explicitly disentangles agent-specific intent and scene-level constraints. Additionally, we introduce an auxiliary lane prediction task to provide targeted supervision for scene understanding and improve latent variable learning. Our approach jointly predicts future trajectories and lane sequences, enabling more interpretable and scene-consistent forecasts. Extensive evaluations on the nuScenes dataset demonstrate the effectiveness of MS-SLV, achieving a 12.37% reduction in average displacement error and a 7.67% reduction in final displacement error over state-of-the-art methods. Moreover, MS-SLV significantly improves multi-modal prediction, reducing the top-5 Miss Rate (MR5) and top-10 Miss Rate (MR10) by 26% and 33%, respectively, and lowering the Off-Road Rate (ORR) by 3%, as compared with the strongest baseline in our evaluation. Full article
(This article belongs to the Special Issue AI-Driven Sensor Technologies for Next-Generation Electric Vehicles)
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