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Recent Advances in Intelligent Vehicle
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Recent Advances in Intelligent Vehicle

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5007

Special Issue Editors

Dr. Biao Yu

E-Mail Website
Guest Editor
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Interests: intelligent vehicles; intelligence test and evaluation
Special Issues, Collections and Topics in MDPI journals
Dr. Jiajia Chen

E-Mail Website
Guest Editor
School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, China
Interests: intelligent vehicles; maneuver decision making; path planning
Special Issues, Collections and Topics in MDPI journals
Dr. Xiang Dong

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.
Interests: underwater image processing; intelligent robots; underwater robots and robot control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent vehicles have been considered an essential way to improve urban mobility and reduce emission pollution and traffic accidents. With the development of artificial intelligence, such as deep learning, intelligent vehicle technologies have obtained enormous success. However, due to the unmature of critical technologies, such as environment perception, motion planning, behavior decision, and motion control, the intelligent vehicle still cannot be deployed to real and complex scenarios.

The intelligent vehicle is a very complicated technical system. Critical technologies from different disciplines, such as sensor technology, pattern recognition, control engineering, artificial intelligence, and vehicle engineering, can affect its performance. This Special Issue explores the recent progress in these related research fields. Welcome topics include, but are not strictly limited to the following:

  • Imaging and sensor technology, such as LiDAR, camera, millimeter wave radar, and so on;
  • Environment perception technology, such as vehicle/pedestrian detection, tracking and prediction, travelable area detection, ground segmentation, and so on;
  • Planning and control technology, such as global planning, local planning, behavior decision, motion control, and so on;
  • Navigation and localization technology, such as lidar odometry, vision odometry, simultaneous localization and mapping (SLAM), and so on;
  • Intelligence test and evaluation.

Dr. Biao Yu
Dr. Jiajia Chen
Dr. Xiang Dong
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 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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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

  • intelligent vehicles
  • environment perception
  • object detection and tracking
  • behavior decision
  • motion planning
  • motion control
  • intelligence test
  • navigation and localization
  • deep learning
  • reinforcement learning

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

Published Papers (5 papers)

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Research

18 pages, 5033 KiB  
Article
Research on Multi-Target Detection and Tracking of Intelligent Vehicles in Complex Traffic Environments Based on Deep Learning Theory
by Xuewen Chen, Shilong Yan and Chenxi Xia
World Electr. Veh. J. 2025, 16(6), 325; https://doi.org/10.3390/wevj16060325 - 11 Jun 2025
Abstract
To address the issues of missed detections and false detections of small target missed detections caused by dense occlusion in complex traffic environments, a non-maximum suppression method, Bot-NMS, is proposed to achieve accurate prediction and localization of occluded targets. In the backbone network [...] Read more.
To address the issues of missed detections and false detections of small target missed detections caused by dense occlusion in complex traffic environments, a non-maximum suppression method, Bot-NMS, is proposed to achieve accurate prediction and localization of occluded targets. In the backbone network of YOLOv7, the Ghost module, the ECA attention mechanism, and the multi-scale feature detection structure are introduced to enhance the network’s capacity to learn small target features. The SCSTD and KITTI datasets were used to train and test the improved YOLOv7 target detection network model. The results demonstrate that the improved YOLOv7 method significantly enhances the recall rate and detection accuracy of various targets. A multi-target tracking method based on target re-identification (ReID) is proposed. Utilizing deep learning theory, a ReID model for target identification is constructed to comprehensively capture global and foreground target features. By establishing the correlation cost matrix of the cosine distance and IoU overlap, the correlation between target detection objects, the tracking trajectory, and ReID feature similarity is realized. The VERI-776 vehicle re-identification dataset and MARKET1501 pedestrian re-identification dataset were used to train the proposed ReID model, and multi-target tracking performance comparison experiments were conducted on the MOT16 dataset. The results show that the multi-target tracking method by introducing the ReID model and improving the cost matrix can better deal with the dense occlusion of the target, and can effectively and accurately track the road target in the realistic complex traffic environment. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicle)
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14 pages, 3525 KiB  
Article
MRD: A Linear-Complexity Encoder for Real-Time Vehicle Detection
by Kaijie Li and Xiaoci Huang
World Electr. Veh. J. 2025, 16(6), 307; https://doi.org/10.3390/wevj16060307 - 30 May 2025
Viewed by 217
Abstract
Vehicle detection algorithms constitute a fundamental pillar in intelligent driving systems and smart transportation infrastructure. Nevertheless, the inherent complexity and dynamic variability of traffic scenarios present substantial technical barriers to robust vehicle detection. While visual transformer-based detection architectures have demonstrated performance breakthroughs through [...] Read more.
Vehicle detection algorithms constitute a fundamental pillar in intelligent driving systems and smart transportation infrastructure. Nevertheless, the inherent complexity and dynamic variability of traffic scenarios present substantial technical barriers to robust vehicle detection. While visual transformer-based detection architectures have demonstrated performance breakthroughs through enhanced perceptual capabilities, establishing themselves as the dominant paradigm in this domain, their practical implementation faces critical challenges due to the quadratic computational complexity inherent in the self-attention mechanism, which imposes prohibitive computational overhead. To address these limitations, this study introduces Mamba RT-DETR (MRD), an optimized architecture featuring three principal innovations: (1) We devise an efficient vehicle detection Mamba (EVDMamba) network that strategically integrates a linear-complexity state space model (SSM) to substantially mitigate computational overhead while preserving feature extraction efficacy. (2) To counteract the constrained receptive fields and suboptimal spatial localization associated with conventional SSM sequence modeling, we implement a multi-branch collaborative learning framework that synergistically optimizes channel dimension processing, thereby augmenting the model’s capacity to capture critical spatial dependencies. (3) Comprehensive evaluations on the BDD100K benchmark demonstrate that MRD architecture achieves a 3.1% enhancement in mean average precision (mAP) relative to state-of-the-art RT-DETR variants, while concurrently reducing parameter count by 55.7%—a dual optimization of accuracy and efficiency. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicle)
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15 pages, 2549 KiB  
Article
SRNeRF: Super-Resolution Neural Radiance Fields for Autonomous Driving Scenario Reconstruction from Sparse Views
by Jun Wang, Xiaojun Zhu, Ziyu Chen, Peng Li, Chunmao Jiang, Hui Zhang, Chennian Yu and Biao Yu
World Electr. Veh. J. 2025, 16(2), 66; https://doi.org/10.3390/wevj16020066 - 23 Jan 2025
Viewed by 984
Abstract
High-fidelity driving scenario reconstruction can generate a lot of realistic virtual simulation environment samples, which can support effective training and testing for autonomous vehicles. Neural radiance fields (NeRFs) have demonstrated their excellence in high-fidelity scenario reconstruction; however, they still rely on dense-view data [...] Read more.
High-fidelity driving scenario reconstruction can generate a lot of realistic virtual simulation environment samples, which can support effective training and testing for autonomous vehicles. Neural radiance fields (NeRFs) have demonstrated their excellence in high-fidelity scenario reconstruction; however, they still rely on dense-view data and precise camera poses, which are difficult to obtain in autonomous vehicles. To address the above issues, we propose a novel approach called SRNeRF, which can eliminate pose-based operations and perform scenario reconstruction from sparse views. To extract more scene knowledge from limited views, we incorporate an image super-resolution module based on a fully convolutional neural network and introduce a new texture loss to capture scene details for higher-quality scene reconstruction. On both object-centric and scene-level datasets, SRNeRF performs comparably to previous methods with ground truth poses and significantly outperforms methods with predicted poses, with a PSNR improvement of about 30%. Finally, we evaluate SRNeRF on our custom autonomous driving dataset, and the results show that SRNeRF can still generate stable images and novel views in the face of sparse views, demonstrating its scalability in autonomous driving scenario synthesis. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicle)
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14 pages, 3119 KiB  
Article
An Adaptive Cruise Control Strategy for Intelligent Vehicles Based on Hierarchical Control
by Di Hu, Jingbo Zhao, Jianfeng Zheng and Haimei Liu
World Electr. Veh. J. 2024, 15(11), 529; https://doi.org/10.3390/wevj15110529 - 15 Nov 2024
Cited by 2 | Viewed by 1545
Abstract
To minimize the occurrence of traffic accidents, such as vehicle rear-end collisions, while enhancing vehicle following, stability, economy, and ride comfort, a hierarchical adaptive cruise control strategy for vehicles is proposed. The upper-level controller computes the desired vehicle output acceleration based on model [...] Read more.
To minimize the occurrence of traffic accidents, such as vehicle rear-end collisions, while enhancing vehicle following, stability, economy, and ride comfort, a hierarchical adaptive cruise control strategy for vehicles is proposed. The upper-level controller computes the desired vehicle output acceleration based on model predictive control and switches between speed and spacing control in accordance with driving conditions. The brake/throttle opening switching model, brake control inverse model, and throttle opening inverse model in the lower-level controller of ACC are designed to obtain the desired throttle opening and braking pressure of the vehicle, thereby achieving control of the vehicle. A joint simulation platform was established using PreScan, CarSim and Matlab/Simulink. Finally, simulations for three typical working conditions were conducted in Simulink to verify the performance of the adaptive cruise control strategy. The results indicate that, in both the constant-speed cruise and vehicle-following cruise conditions, the vehicle can rapidly and stably follow the set initial speed and consistently maintain a safe distance from the preceding vehicle. Under the emergency braking condition, the vehicle can promptly respond with deceleration, ensuring driving safety. The proposed control strategy can accurately and safely track the target vehicle in diverse driving conditions and can concurrently fulfill the requirements of economy and comfort during vehicle travel. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicle)
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13 pages, 2762 KiB  
Article
Advanced Point Cloud Techniques for Improved 3D Object Detection: A Study on DBSCAN, Attention, and Downsampling
by Wenqiang Zhang, Xiang Dong, Jingjing Cheng and Shuo Wang
World Electr. Veh. J. 2024, 15(11), 527; https://doi.org/10.3390/wevj15110527 - 15 Nov 2024
Cited by 1 | Viewed by 1577
Abstract
To address the challenges of limited detection precision and insufficient segmentation of small to medium-sized objects in dynamic and complex scenarios, such as the dense intermingling of pedestrians, vehicles, and various obstacles in urban environments, we propose an enhanced methodology. Firstly, we integrated [...] Read more.
To address the challenges of limited detection precision and insufficient segmentation of small to medium-sized objects in dynamic and complex scenarios, such as the dense intermingling of pedestrians, vehicles, and various obstacles in urban environments, we propose an enhanced methodology. Firstly, we integrated a point cloud processing module utilizing the DBSCAN clustering algorithm to effectively segment and extract critical features from the point cloud data. Secondly, we introduced a fusion attention mechanism that significantly improves the network’s capability to capture both global and local features, thereby enhancing object detection performance in complex environments. Finally, we incorporated a CSPNet downsampling module, which substantially boosts the network’s overall performance and processing speed while reducing computational costs through advanced feature map segmentation and fusion techniques. The proposed method was evaluated using the KITTI dataset. Under moderate difficulty, the BEV mAP for detecting cars, pedestrians, and cyclists achieved 87.74%, 55.07%, and 67.78%, reflecting improvements of 1.64%, 5.84%, and 5.53% over PointPillars. For 3D mAP, the detection accuracy for cars, pedestrians, and cyclists reached 77.90%, 49.22%, and 62.10%, with improvements of 2.91%, 5.69%, and 3.03% compared to PointPillars. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicle)
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