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Sensor Fusion for the Safety of Automated Driving Systems

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

Deadline for manuscript submissions: 10 April 2026 | Viewed by 12528

Special Issue Editors


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Guest Editor
School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: authentication; automated driving security; mobile security

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Guest Editor
Institute for Cyber Security, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: software security; network security
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
Interests: program security; threat detection; AI security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The large-scale deployment of autonomous vehicles has been continually delayed due to safety concerns. To enhance the safety of automated driving systems, sensor fusion plays a crucial role in integrating data from various sensors like cameras, LiDAR, radar, ultrasonic, GPS, and IMUs. By combining information from these sensors, automated vehicles can have a more comprehensive understanding of their surroundings, enabling better decision-making and improving overall system reliability. Sensor fusion helps in accurately detecting objects, predicting their movements, and ensuring the vehicle operates safely in diverse driving conditions.

This Special Issue was conceived to further the state-of-the-art in sensor fusion architecture, technology, and algorithms for autonomous driving to achieve safety, robustness, and efficiency in autonomous vehicles. In recent years, sensor fusion technology has made great progress and promoted the development of safety and reliability in autonomous driving. However, these technologies have strict autonomous driving system hardware and software requirements, which inevitably increases the complexity of the systems. To this end, we welcome submissions of various advanced multi-sensor fusion approaches for perception tasks such as object detection, semantic segmentation, object classification, depth completion, and prediction, which can be used to improve the safety and robustness of autonomous driving systems while keeping costs as controllable as possible.

Dr. Man Zhou
Dr. Weina Niu
Dr. Xin Liu
Guest Editors

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Keywords

  • autonomous driving
  • safety and robustness
  • autonomous driving system security
  • sensor data security
  • sensor fusion architecture
  • cameras
  • LiDAR
  • radar

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Published Papers (2 papers)

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Research

19 pages, 3470 KB  
Article
Driver Monitoring System Using Computer Vision for Real-Time Detection of Fatigue, Distraction and Emotion via Facial Landmarks and Deep Learning
by Tamia Zambrano, Luis Arias, Edgar Haro, Victor Santos and María Trujillo-Guerrero
Sensors 2026, 26(3), 889; https://doi.org/10.3390/s26030889 - 29 Jan 2026
Viewed by 249
Abstract
Car accidents remain a leading cause of death worldwide, with drowsiness and distraction accounting for roughly 25% of fatal crashes in Ecuador. This study presents a real-time driver monitoring system that uses computer vision and deep learning to detect fatigue, distraction, and emotions [...] Read more.
Car accidents remain a leading cause of death worldwide, with drowsiness and distraction accounting for roughly 25% of fatal crashes in Ecuador. This study presents a real-time driver monitoring system that uses computer vision and deep learning to detect fatigue, distraction, and emotions from facial expressions. It combines a MobileNetV2-based CNN trained on RAF-DB for emotion recognition and MediaPipe’s 468 facial landmarks to compute the EAR (Eye Aspect Ratio), the MAR (Mouth Aspect Ratio), the gaze, and the head pose. Tests with 27 participants in both real and simulated driving environments showed strong results. There was a 100% accuracy in detecting distraction, 85.19% for yawning, and 88.89% for eye closure. The system also effectively recognized happiness (100%) and anger/disgust (96.3%). However, it struggled with sadness and failed to detect fear, likely due to the subtlety of real-world expressions and limitations in the training dataset. Despite these challenges, the results highlight the importance of integrating emotional awareness into driver monitoring systems, which helps reduce false alarms and improve response accuracy. This work supports the development of lightweight, non-invasive technologies that enhance driving safety through intelligent behavior analysis. Full article
(This article belongs to the Special Issue Sensor Fusion for the Safety of Automated Driving Systems)
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14 pages, 2906 KB  
Article
Real-Time Fatigue Detection Algorithms Using Machine Learning for Yawning and Eye State
by Fazliddin Makhmudov, Dilmurod Turimov, Munis Xamidov, Fayzullo Nazarov and Young-Im Cho
Sensors 2024, 24(23), 7810; https://doi.org/10.3390/s24237810 - 6 Dec 2024
Cited by 23 | Viewed by 11450
Abstract
Drowsiness while driving is a major factor contributing to traffic accidents, resulting in reduced cognitive performance and increased risk. This article gives a complete analysis of a real-time, non-intrusive sleepiness detection system based on convolutional neural networks (CNNs). The device analyses video data [...] Read more.
Drowsiness while driving is a major factor contributing to traffic accidents, resulting in reduced cognitive performance and increased risk. This article gives a complete analysis of a real-time, non-intrusive sleepiness detection system based on convolutional neural networks (CNNs). The device analyses video data recorded from an in-vehicle camera to monitor drivers’ facial expressions and detect fatigue indicators such as yawning and eye states. The system is built on a strong architecture and was trained using a diversified dataset under varying lighting circumstances and facial angles. It uses Haar cascade classifiers for facial area extraction and advanced image processing algorithms for fatigue diagnosis. The results demonstrate that the system obtained a 96.54% testing accuracy, demonstrating the efficiency of using behavioural indicators such as yawning frequency and eye state detection to improve performance. The findings show that CNN-based architectures can address major public safety concerns, such as minimizing accidents caused by drowsy driving. This study not only emphasizes the need of deep learning in establishing dependable and practical driver monitoring systems, but it also lays the groundwork for future improvements, such as the incorporation of new behavioural and physiological measurements. The suggested solution is a big step towards increasing road safety and reducing the risks associated with driver weariness. Full article
(This article belongs to the Special Issue Sensor Fusion for the Safety of Automated Driving Systems)
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