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Applied Sciences
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Advances in Autonomous Driving: Detection and Tracking
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Advances in Autonomous Driving: Detection and Tracking

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 3782

Special Issue Editors

Dr. Jhonghyun An

E-Mail Website
Guest Editor
School of Computing, Gachon University, Seongnam-si 1332, Republic of Korea
Interests: autonomous vehicle; AI; machine learning
Prof. Dr. Dimitri Konstantas

E-Mail Website
Guest Editor
Geneva School of Economics and Management, University of Geneva, 1211 Geneva, Switzerland
Interests: autonomous vehicles; information security; mobile applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous driving technologies have developed rapidly over the past decade, yet their perception of dynamic environments remains one of the most critical challenges. The detection and tracking of surrounding objects—such as vehicles, pedestrians, cyclists, and obstacles—are essential for safe and efficient autonomous navigation. Recent developments in computer vision, deep learning, sensor fusion, and 3D scene understanding have significantly enhanced perception systems, enabling more accurate and robust decision-making in complex environments.

This Special Issue aims to gather cutting-edge research related to object detection, multi-object tracking, sensor-based environmental perception, and the integration of these technologies into autonomous driving systems. Topics of interest include, but are not limited to, novel algorithmic approaches, real-time implementation, dataset development, domain adaptation, robustness under challenging conditions (e.g., adverse weather, occlusion), and cross-modal sensor fusion (e.g., LiDAR, radar, camera). Through this Special Issue, we seek to foster academic and industrial collaboration, promoting new ideas that drive the future of autonomous driving forward.

Dr. Jhonghyun An
Prof. Dr. Dimitri Konstantas
Guest Editors

Manuscript Submission Information

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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 driving
  • end-to-end learning
  • object detection and multi-object tracking
  • sensor fusion
  • 3D perception
  • adverse weather robustness
  • deep learning for perception
  • real-time systems
  • domain adaptation
  • cross-modal learning

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Published Papers (3 papers)

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Research

29 pages, 5908 KB  
Article
Dual-Linear Attention Network for Multi-Object Tracking and Segmentation
by Yiqing Ren, Xuedong Wu and Haohao Fu
Appl. Sci. 2026, 16(1), 65; https://doi.org/10.3390/app16010065 - 20 Dec 2025
Viewed by 621
Abstract
Multi-object tracking and segmentation (MOTS) is a critical task in video analysis with applications spanning autonomous driving, robot navigation, and scene understanding. MOTS has made significant progress but still faces persistent challenges, such as crowded scenes, abnormal illumination, and small objects. Several trackers [...] Read more.
Multi-object tracking and segmentation (MOTS) is a critical task in video analysis with applications spanning autonomous driving, robot navigation, and scene understanding. MOTS has made significant progress but still faces persistent challenges, such as crowded scenes, abnormal illumination, and small objects. Several trackers have implemented attention mechanisms to overcome these difficulties. However, many attention mechanisms have quadratic computational complexity and use little spatio-temporal information. This paper proposes a Dual-Linear Attention Network (DLAN), a novel approach that effectively integrates both appearance and spatio-temporal information while maintaining linear attention complexity. DLAN employs recursive linear self-attention to strengthen the appearance representation and prototypical linear cross-attention to condense rich spatio-temporal information, which can compensate for missing pixel information. DLAN optimizes both image features and segmentation, with the refined segmentation guiding frame-level memory updates to improve instance consistency. Extensive experiments on BDD100K MOT, BDD100K MOTS, and KITTI MOTS datasets demonstrate the following: (1) The three main challenges of object occlusion, illumination variation, and distant objects have been successfully mitigated by integrating DLAN. (2) DLAN has achieved an overall competitive performance when compared to state-of-the-art trackers, with a 26% reduction in identity switches (IDS) when compared to QDTrack-mots-fix. Full article
(This article belongs to the Special Issue Advances in Autonomous Driving: Detection and Tracking)
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21 pages, 7916 KB  
Article
Radar-Only Cooperative Adaptive Cruise Control Under Acceleration Disturbances: ACC, KF-CACC, and Multi-Q IMM-KF CACC
by Jihun Lim, Guntae Kim, Cheolmin Jeong and Changmook Kang
Appl. Sci. 2025, 15(22), 12199; https://doi.org/10.3390/app152212199 - 17 Nov 2025
Viewed by 658
Abstract
The rapid increase in global vehicle usage has intensified challenges such as traffic congestion, frequent accidents, and energy consumption, highlighting the need for safe and efficient platooning strategies. Conventional adaptive cruise control (ACC), while widely adopted, suffers from string instability that amplifies disturbances [...] Read more.
The rapid increase in global vehicle usage has intensified challenges such as traffic congestion, frequent accidents, and energy consumption, highlighting the need for safe and efficient platooning strategies. Conventional adaptive cruise control (ACC), while widely adopted, suffers from string instability that amplifies disturbances along a platoon. Communication-based cooperative ACC (CACC) can theoretically guarantee string stability at short headways, but its dependence on costly and unreliable vehicle-to-vehicle (V2V) links limits large-scale deployment. Radar-only CACC using single-model Kalman Filter (KF) alleviates this dependency, yet its estimation accuracy degrades under abrupt maneuvers due to model mismatch. To overcome these limitations, this paper proposes a Multi-Q Interacting Multiple Model Kalman Filter (Multi-Q IMM-KF) approach that adaptively blends multiple motion models to ensure robust acceleration estimation across diverse driving conditions. A four-vehicle platoon simulation in CarSim–Simulink demonstrates that the Multi-Q IMM-KF CACC significantly reduces spacing error propagation and improves velocity tracking compared with ACC and Nominal KF-CACC, offering a cost-effective and communication-resilient solution for practical platoon control. Full article
(This article belongs to the Special Issue Advances in Autonomous Driving: Detection and Tracking)
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17 pages, 2309 KB  
Article
Robust Visual–Inertial Odometry via Multi-Scale Deep Feature Extraction and Flow-Consistency Filtering
by Hae Min Cho
Appl. Sci. 2025, 15(20), 10935; https://doi.org/10.3390/app152010935 - 11 Oct 2025
Cited by 1 | Viewed by 2107
Abstract
We present a visual–inertial odometry (VIO) system that integrates a deep feature extraction and filtering strategy with optical flow to improve tracking robustness. While many traditional VIO methods rely on hand-crafted features, they often struggle to remain robust under challenging visual conditions, such [...] Read more.
We present a visual–inertial odometry (VIO) system that integrates a deep feature extraction and filtering strategy with optical flow to improve tracking robustness. While many traditional VIO methods rely on hand-crafted features, they often struggle to remain robust under challenging visual conditions, such as low texture, motion blur, or lighting variation. These methods tend to exhibit large performance variance across different environments, primarily due to the limited repeatability and adaptability of hand-crafted keypoints. In contrast, learning-based features offer richer representations and can generalize across diverse domains thanks to data-driven training. However, they often suffer from uneven spatial distribution and temporal instability, which can degrade tracking performance. To address these issues, we propose a hybrid front-end that combines a lightweight deep feature extractor with an image pyramid and grid-based keypoint sampling to enhance spatial diversity. Additionally, a forward–backward optical-flow-consistency check is applied to filter unstable keypoints. The system improves feature tracking stability by enforcing spatial and temporal consistency while maintaining real-time efficiency. Finally, the effectiveness of the proposed VIO system is validated on the EuRoC MAV benchmark, showing a 19.35% reduction in trajectory RMSE and improved consistency across multiple sequences compared with previous methods. Full article
(This article belongs to the Special Issue Advances in Autonomous Driving: Detection and Tracking)
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