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Electronics
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Autonomous Vehicles: Sensing, Mapping, and Positioning
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Autonomous Vehicles: Sensing, Mapping, and Positioning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 August 2026 | Viewed by 792

Special Issue Editors

Prof. Dr. Felix Huening

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, University of Applied Sciences Aachen, 52066 Aachen, Germany
Interests: sensors; embedded systems for automotive and IoT applications; bus communication in vehicles; autonomous driving
Prof. Dr. Toni Viscido

E-Mail Website
Guest Editor
Faculty of Automotive Engineering and Production, University of Applied Sciences Cologne, 50679 Cologne, Germany
Interests: automotive electrics and electronics; diagnostic systems and applications; data-driven mobility and data-based business models; autonomous and connected driving
Prof. Dr. Alexander Ferrein

E-Mail Website
Guest Editor
Mobile Autonomous Systems & Cognitive Robotics Institue, FH Aachen University of Applied Sciences, 52056 Aachen, Germany
Interests: mobile robotics; intelligent systems; self-driving cars; high-level control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous vehicles are transforming the future of mobility, relying on advanced technologies to perceive and navigate complex environments safely and efficiently. This Special Issue focuses on cutting-edge research in sensing, mapping, and positioning systems that enable robust and intelligent vehicle autonomy as well as intra- and inter-vehicle communication systems covering legal requirements of the EU data act as well.

We seek original and high-quality papers from industrial and scientific sources that explore the use of diverse sensors—such as LiDAR, radar, cameras, and GNSS—for accurate environmental perception and situational awareness. Topics of interest include, but are not limited to, sensor fusion, environmental modeling, real-time mapping, and control- and/or AI-based algorithms for localization and pose estimation in dynamic and challenging conditions.

Contributions that advance the theoretical foundations or practical implementations of these technologies, including AI-driven perception systems and cooperative positioning methods, are especially encouraged. By bringing together developments across hardware and software domains, the journal aims to foster innovation that supports the safe and scalable deployment of autonomous vehicles.

Prof. Dr. Felix Huening
Prof. Dr. Toni Viscido
Prof. Dr. Alexander Ferrein
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-blind 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

  • autonomous vehicles
  • environment perception
  • situational awareness
  • sensor fusion
  • environmental modeling
  • real-time mapping
  • sensors for autonomous driving
  • localization
  • pose estimation

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Published Papers (2 papers)

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23 pages, 2619 KB  
Article
LITransformer: Transformer-Based Vehicle Trajectory Prediction Integrating Spatio-Temporal Attention Networks with Lane Topology and Dynamic Interaction
by Yuanchao Zhong, Zhiming Gui, Zhenji Gao, Xinyu Wang and Jiawen Wei
Electronics 2025, 14(24), 4950; https://doi.org/10.3390/electronics14244950 - 17 Dec 2025
Viewed by 171
Abstract
Vehicle trajectory prediction is a pivotal technology in intelligent transportation systems. Existing methods encounter challenges in effectively modeling lane topology and dynamic interaction relationships in complex traffic scenarios, limiting prediction accuracy and reliability. This paper presents Lane Interaction Transformer (LITransformer), a lane-informed trajectory [...] Read more.
Vehicle trajectory prediction is a pivotal technology in intelligent transportation systems. Existing methods encounter challenges in effectively modeling lane topology and dynamic interaction relationships in complex traffic scenarios, limiting prediction accuracy and reliability. This paper presents Lane Interaction Transformer (LITransformer), a lane-informed trajectory prediction framework that builds on spatio–temporal graph attention networks and Transformer-based global aggregation. Rather than introducing entirely new network primitives, LITransformer focuses on two design aspects: (i) a lane topology encoder that fuses geometric and semantic lane features via direction-sensitive, multi-scale dilated graph convolutions, converting vectorized lane data into rich topology-aware representations; and (ii) an Interaction-Aware Graph Attention mechanism (IAGAT) that explicitly models four types of interactions between vehicles and lane infrastructure (V2V, V2N, N2V, N2N), with gating-based fusion of structured road constraints and dynamic spatio–temporal features. The overall architecture employs a Transformer module to aggregate global scene context and a multi-modal decoding head to generate diverse trajectory hypotheses with confidence estimation. Extensive experiments on the Argoverse dataset show that LITransformer achieves a minADE of 0.76 and a minFDE of 1.20, and significantly outperforms representative baselines such as LaneGCN and HiVT. These results demonstrate that explicitly incorporating lane topology and interaction-aware spatio-temporal modeling can significantly improve the accuracy and reliability of vehicle trajectory prediction in complex real-world traffic scenarios. Full article
(This article belongs to the Special Issue Autonomous Vehicles: Sensing, Mapping, and Positioning)
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15 pages, 1071 KB  
Article
Analysis of Automotive Lidar Corner Cases Under Adverse Weather Conditions
by Behrus Alavi, Thomas Illing, Felician Campean, Paul Spencer and Amr Abdullatif
Electronics 2025, 14(23), 4695; https://doi.org/10.3390/electronics14234695 - 28 Nov 2025
Viewed by 344
Abstract
The validation of sensor systems, particularly lidar, is crucial in advancing autonomous vehicle technology. Despite their robust perception capabilities, certain weather conditions and object characteristics can challenge detection performance, leading to potential safety concerns. This study investigates corner cases where object detection may [...] Read more.
The validation of sensor systems, particularly lidar, is crucial in advancing autonomous vehicle technology. Despite their robust perception capabilities, certain weather conditions and object characteristics can challenge detection performance, leading to potential safety concerns. This study investigates corner cases where object detection may fail due to physical constraints. Utilizing virtual testing environments like Carla and ROS2, simulations analyze how reflection characteristics affect detectability by implementing weather models into a real-time simulation. Results reveal challenges in detecting black objects compared to white ones, particularly in adverse weather conditions. A time-sensitive corner case was analyzed, revealing that while bad weather and wet roads restrict the safe driving speed range, complete deactivation of the driving assistant at certain speeds may be unnecessary despite current manufacturer practices. The study underscores the importance of considering such factors in future safety protocols to mitigate accidents and ensure reliable autonomous driving systems. Full article
(This article belongs to the Special Issue Autonomous Vehicles: Sensing, Mapping, and Positioning)
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