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Application of Advanced Perception Technology in Vehicle Intelligent Control

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

Deadline for manuscript submissions: closed (31 July 2025) | Viewed by 820

Special Issue Editors


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Guest Editor
College of Automotive Engineering, Jilin University, Changchun 130012, China
Interests: the key technologies of new energy vehicles; the intelligent networked vehicle planning and intelligent control methods; the testing and evaluation techniques of intelligent networked vehicles
Special Issues, Collections and Topics in MDPI journals
College of Automotive Engineering, Jilin University, Changchun 130012, China
Interests: electric vehicles
Special Issues, Collections and Topics in MDPI journals
State Key Laboratory of Automotive Simulation and Control, Jilin university, Changchun 130025, China
Interests: key technologies of new energy vehicles; computer vision; new energy vehicle electric wheel
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The era of intelligent vehicles has dawned upon us. The advanced intelligent vehicle control system will continue to evolve, fueled by the adoption of artificial intelligence technology, the advent of novel sensors, and the ongoing progression of related technologies. The primary objective of intelligent vehicle control systems is to leverage application systems, control theory, and intelligent computing to address both present and emerging mobility challenges, thereby guaranteeing that ground vehicles operate safely, comfortably, in an eco-friendly manner, and autonomously. To accomplish this, it is imperative to develop increasingly precise dynamic vehicle models and integrate advanced intelligent sensors along with sensor fusion technologies, enabling an accurate comprehension of the vehicle's status.

This Special Issue focuses on sharing the latest achievements in vehicle sensors, aiming to promote the progression of advanced vehicle control systems and achieve more sustainable transportation application goals.

The topics of interest include, but are not limited to, the following:

  • Vehicle dynamics control;
  • Autonomous driving systems;
  • Driver assistance systems;
  • Intelligent sensors and actuators;
  • Driver–vehicle systems;
  • Electric vehicles;
  • Integrated vehicle control;
  • Sensor fusion;
  • Vehicle sensors/actuators;
  • Autonomous navigation systems;
  • Autonomous positioning systems;
  • Road traffic detection;
  • Environment perception;
  • Unmanned vehicles.

Prof. Dr. Pengyu Wang
Dr. Jianhua Li
Dr. Feng Xiao
Guest Editors

Manuscript Submission Information

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Keywords

  • intelligent control
  • vehicle
  • advanced perception technology

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Published Papers (1 paper)

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Research

21 pages, 2434 KB  
Article
MBFILNet: A Multi-Branch Detection Network for Autonomous Mining Trucks in Dusty Environments
by Fei-Xiang Xu, Di-Long Zhu, Yu-Peng Hu, Rui Zhang and Chen Zhou
Sensors 2025, 25(17), 5324; https://doi.org/10.3390/s25175324 - 27 Aug 2025
Viewed by 502
Abstract
As a critical technology of autonomous mining trucks, object detection directly determines system safety and operational reliability. However, autonomous mining trucks often work in dusty open-pit environments, in which dusty interference significantly degrades the accuracy of object detection. To overcome the problem mentioned [...] Read more.
As a critical technology of autonomous mining trucks, object detection directly determines system safety and operational reliability. However, autonomous mining trucks often work in dusty open-pit environments, in which dusty interference significantly degrades the accuracy of object detection. To overcome the problem mentioned above, a multi-branch feature interaction and location detection network (MBFILNet) is proposed in this study, consisting of multi-branch feature interaction with differential operation (MBFI-DO) and depthwise separable convolution-enhanced non-local attention (DSC-NLA). On one hand, MBFI-DO not only strengthens the extraction of channel-wise semantic features but also improves the representation of salient features of images with dusty interference. On the other hand, DSC-NLA is used to capture long-range spatial dependencies to focus on target-object structural information. Furthermore, a custom dataset called Dusty Open-pit Mining (DOM) is constructed, which is augmented using a cycle-consistent generative adversarial network (CycleGAN). Finally, a large number of experiments based on DOM are conducted to evaluate the performance of MBFILNet in dusty open-pit environments. The results show that MBFILNet achieves a mean Average Precision (mAP) of 72.0% based on the DOM dataset, representing a 1.3% increase compared to the Featenhancer model. Moreover, in comparison with YOLOv8, there is an astounding 2% increase in the mAP based on MBFILNet, demonstrating detection accuracy in dusty open-pit environments can be effectively improved with the method proposed in this paper. Full article
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