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Sensors
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Perception Sensors and Sensor Fusion for Intelligent and Autonomous Agents
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Perception Sensors and Sensor Fusion for Intelligent and Autonomous Agents

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Vehicular Sensing".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 560

Editors

Dr. Wei Han

E-Mail Website
Guest Editor
College of Automotive and Energy Engineering, Tongji University, Shanghai, China
Interests: visual perception and dynamics control of autonomous vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Dequan Zeng

E-Mail Website
Guest Editor
School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, China
Interests: key technologies of intelligent networked new energy vehicles (perception, decision-making, planning, control, energy management, multi-agent scheduling); all-terrain special vehicle control technolog
Dr. Junfei Nie

E-Mail Website
Guest Editor
School of Electrical Engineering, Shaoyang University, Shaoyang, China
Interests: sensing; control and diagnosis of intelligent systems
Dr. Akos Odry

E-Mail Website
Guest Editor
Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, Szeged, Hungary
Interests: inertial sensors; sensor fusion; localization; intelligent control; robotics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Larisa Rybak

E-Mail Website
Guest Editor
International Research Laboratory Intelligent Robotic Systems and Technologies, Belgorod State Technological University Named After V.G. Shukhov, 308012 Belgorod, Russia
Interests: manipulators; parallel robots; robotics; mechanics; mobile robots
Special Issues, Collections and Topics in MDPI journals
Dr. Dmitry Malyshev

E-Mail Website
Guest Editor
Research Institute Robotics and Control Systems, Belgorod State Technological University n.a. V.G. Shukhov, Kostyukova 46, 308012 Belgorod, Russia
Interests: robotics; simulation; parallel robot; lower limb; upper limb

Special Issue Information

Dear Colleagues,

The rapid advancement of intelligent and autonomous agents, ranging from autonomous vehicles and driver assistance systems to intelligent robotics and new energy systems, has placed unprecedented demands on real-time, accurate and robust environmental perception. Single-sensor solutions often suffer from inherent limitations and environmental disturbances, making them insufficient for reliable operation in complex, dynamic scenarios. Multi-source information fusion and enhancement management have emerged as key enablers to overcome these challenges. By synergistically combining data from diverse perception sensors (e.g., cameras, LiDAR, radar, IMU) and leveraging neural-enhanced fusion techniques, it becomes possible to achieve superior situational awareness, object recognition and state estimation, thereby empowering intelligent agents with enhanced decision-making and cooperative control capabilities.

This Special Issue aims to collect original research and review articles that focus on perception sensors and sensor fusion technologies for intelligent and autonomous agents. We particularly welcome contributions on novel perception sensor designs and calibration; neural-enhanced and learning-based sensor fusion frameworks; autonomous driving and driver assistance systems; perception solutions for intelligent new energy systems and autonomous robotics; multi-agent information interaction, information enhancement and multi-agent cooperative control as well as multi-source information fusion management theories and practical implementations. Case studies on real-world autonomous systems, robustness validation in challenging environments and scalable fusion architectures are also highly encouraged.

We believe this Special Issue will provide valuable insights into cutting-edge perception and fusion technologies, fostering cross-disciplinary knowledge exchange among academia, industry and system integrators to accelerate the deployment of next-generation intelligent and autonomous agents.

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

  • [Topic 1] Perception Sensors, such as visual sensing, spectral sensing, etc.
  • [Topic 2] Intelligent and autonomous agents and robotics
  • [Topic 3] Autonomous driving and driver assistance
  • [Topic 4] Neural-enhanced sensor fusion
  • [Topic 5] Intelligent new energy system
  • [Topic 6] Intelligent motor control and fault diagnosis
  • [Topic 7] Multi-agent information interaction and cooperative control
  • [Topic 8] Information enhancement
  • [Topic 9] Electrical integration, sensing and control of wheel corner module
  • [Topic 10] Multi-source information fusion management

Dr. Wei Han
Dr. Dequan Zeng
Dr. Junfei Nie
Dr. Akos Odry
Prof. Dr. Larisa Rybak
Dr. Dmitry Malyshev
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. 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

  • perception sensors and sensor fusion
  • intelligent and autonomous agents
  • autonomous driving and driver assistance
  • neural-enhanced sensor fusion
  • intelligent new energy system
  • intelligent and autonomous robotics
  • multi-agent information interaction
  • information enhancement
  • multi-agent cooperative control
  • multi-source information fusion management

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

28 pages, 19797 KB  
Article
An LOSM Speed Controller for Autonomous Commercial Vehicles Addressing Disturbance from Load and Slope Uncertainty
by Jinwen Yang, Huafu Fang, Ju Lu, Lingang Yang, Zhiqiang Jiang and Giuseppe Carbone
Sensors 2026, 26(16), 5203; https://doi.org/10.3390/s26165203 - 17 Aug 2026
Viewed by 250
Abstract
Autonomous commercial vehicles (ACVs) frequently encounter drastic variations in payload and complex road conditions during practical operations. Consequently, effectively suppressing external disturbances caused by payload and road slope uncertainties has become a critical challenge in enhancing the robustness of their low-level control systems. [...] Read more.
Autonomous commercial vehicles (ACVs) frequently encounter drastic variations in payload and complex road conditions during practical operations. Consequently, effectively suppressing external disturbances caused by payload and road slope uncertainties has become a critical challenge in enhancing the robustness of their low-level control systems. To address this issue, this paper proposes a sliding mode control (SMC) strategy based on Luenberger observer disturbance compensation (LOSM), aiming to simultaneously mitigate the adverse effects of these two uncertainties on the vehicle’s speed control performance. First, according to the driving characteristics of commercial vehicles, a full-condition longitudinal dynamic model encompassing uphill, downhill, and flat road scenarios is established. Second, by deeply integrating the Luenberger observer with sliding mode control theory, an active disturbance rejection LOSM speed controller is designed. Furthermore, the boundary conditions for the closed-loop system to achieve asymptotic stability are rigorously derived and proven using Lyapunov functions. Finally, to comprehensively verify the effectiveness of the proposed strategy, eight typical testing scenarios are constructed, and three benchmark algorithms—PI control, radial basis function adaptive sliding mode (RBFSM) control, and radial basis function backstepping sliding mode (RBFBSSM) control are introduced for comparative analysis. The validation results demonstrate that although all four methods can achieve speed tracking and suppress disturbances, the proposed LOSM strategy exhibits the optimal comprehensive performance across various scenarios. Specifically, its steady-state mean error is typically maintained below 2.5%, and it yields the minimum steady-state variance in the majority of scenarios. These results demonstrate that the designed LOSM method can significantly improve the precision and smoothness of ACVs’ speed control under the dual disturbances of unknown mass and road slope. Full article
(This article belongs to the Special Issue Perception Sensors and Sensor Fusion for Intelligent and Autonomous Agents)
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