Modeling, Design, Analysis and Management of Embedded Control Systems for Automated Driving

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 7659

Special Issue Editors

Department of Machine Design, KTH Royal Institute of Technology, SE 100 44 Stockholm, Sweden
Interests: systems theory; architecture design; ontological engineering; domain-specific language; logic programming; anomaly detection; safety engineering
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Co-Guest Editor
Department of Electrification and Reliability, RISE Research Institutes of Sweden, SE 501 15 Borås, Sweden
Interests: dependability engineering; safety and security assurance; automated systems

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Co-Guest Editor
Department of Electrification and Reliability, RISE Research Institutes of Sweden, SE 501 15 Borås, Sweden
Interests: system architecture; automated systems; safety and security assurance; verification and validation of automated driving systems; product-line management

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Co-Guest Editor
Functional Safety Expert, Qamcom Research and Technology AB, SE 583 30 Linköping, Sweden
Interests: systems engineering; contract theory; safety engineering; methodology, automotive perception systems

Special Issue Information

Dear Colleagues,

Embedded Control Systems (ECS) constitute the key enabling technology for autonomous agents and multi-agent systems. The aim is to allow a wide range of intelligent features, relating to the operation perception, situation reasoning, action planning and actuation of physical processes and human behaviors, through the integration of advanced functions, embedded software and hardware. Many of these intelligent features are, however, safety or mission critical, as the errors can result in system failures with unreasonable risks. This calls on the one hand for effective engineering methods and tools for correct by construction, verification and validation; and, on the other hand, for advanced technologies for observing and analyzing various actual operational feedbacks. 

This Special Issue focuses on the modeling, design, analysis and management of ECS for Automated Driving (AD) vehicles. We solicit high-quality articles presenting original and unpublished results of conceptual, theoretical, and empirical research relating to system specifications, quality management, safety and reliability engineering. We also especially welcome industrial insights, experiences and practices regarding post-deployment-time data collection and analysis for safety certification, product-line and lifecycle management.

Dr. Dejiu Chen
Dr. Fredrik Warg
Dr. Anders Thorsén
Dr. Anders Cassel
Guest Editors

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Keywords

  • knowledge engineering
  • ontological engineering
  • system modeling and analysis
  • architecture design and optimization
  • simulation, formal methods
  • variability modeling and analysis
  • operational and functional safety engineering
  • reliability engineering
  • contract theory and component-based engineering
  • self-adaptation
  • XAI and data-driven analysis
  • lifecycle management

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Published Papers (4 papers)

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Research

20 pages, 13167 KiB  
Article
O2SAT: Object-Oriented-Segmentation-Guided Spatial-Attention Network for 3D Object Detection in Autonomous Vehicles
by Husnain Mushtaq, Xiaoheng Deng, Irshad Ullah, Mubashir Ali and Babur Hayat Malik
Information 2024, 15(7), 376; https://doi.org/10.3390/info15070376 - 28 Jun 2024
Cited by 2 | Viewed by 978
Abstract
Autonomous vehicles (AVs) strive to adapt to the specific characteristics of sustainable urban environments. Accurate 3D object detection with LiDAR is paramount for autonomous driving. However, existing research predominantly relies on the 3D object-based assumption, which overlooks the complexity of real-world road environments. [...] Read more.
Autonomous vehicles (AVs) strive to adapt to the specific characteristics of sustainable urban environments. Accurate 3D object detection with LiDAR is paramount for autonomous driving. However, existing research predominantly relies on the 3D object-based assumption, which overlooks the complexity of real-world road environments. Consequently, current methods experience performance degradation when targeting only local features and overlooking the intersection of objects and road features, especially in uneven road conditions. This study proposes a 3D Object-Oriented-Segmentation Spatial-Attention (O2SAT) approach to distinguish object points from road points and enhance the keypoint feature learning by a channel-wise spatial attention mechanism. O2SAT consists of three modules: Object-Oriented Segmentation (OOS), Spatial-Attention Feature Reweighting (SFR), and Road-Aware 3D Detection Head (R3D). OOS distinguishes object and road points and performs object-aware downsampling to augment data by learning to identify the hidden connection between landscape and object; SFR performs weight augmentation to learn crucial neighboring relationships and dynamically adjust feature weights through spatial attention mechanisms, which enhances the long-range interactions and contextual feature discrimination for noise suppression, improving overall detection performance; and R3D utilizes refined object segmentation and optimized feature representations. Our system forecasts prediction confidence into existing point-backbones. Our method’s effectiveness and robustness across diverse datasets (KITTI) has been demonstrated through vast experiments. The proposed modules seamlessly integrate into existing point-based frameworks, following a plug-and-play approach. Full article
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26 pages, 9324 KiB  
Article
Architectural Framework to Enhance Image-Based Vehicle Positioning for Advanced Functionalities
by Iosif-Alin Beti, Paul-Corneliu Herghelegiu and Constantin-Florin Caruntu
Information 2024, 15(6), 323; https://doi.org/10.3390/info15060323 - 31 May 2024
Cited by 2 | Viewed by 1070
Abstract
The growing number of vehicles on the roads has resulted in several challenges, including increased accident rates, fuel consumption, pollution, travel time, and driving stress. However, recent advancements in intelligent vehicle technologies, such as sensors and communication networks, have the potential to revolutionize [...] Read more.
The growing number of vehicles on the roads has resulted in several challenges, including increased accident rates, fuel consumption, pollution, travel time, and driving stress. However, recent advancements in intelligent vehicle technologies, such as sensors and communication networks, have the potential to revolutionize road traffic and address these challenges. In particular, the concept of platooning for autonomous vehicles, where they travel in groups at high speeds with minimal distances between them, has been proposed to enhance the efficiency of road traffic. To achieve this, it is essential to determine the precise position of vehicles relative to each other. Global positioning system (GPS) devices have an intended positioning error that might increase due to various conditions, e.g., the number of available satellites, nearby buildings, trees, driving into tunnels, etc., making it difficult to compute the exact relative position between two vehicles. To address this challenge, this paper proposes a new architectural framework to improve positioning accuracy using images captured by onboard cameras. It presents a novel algorithm and performance results for vehicle positioning based on GPS and video data. This approach is decentralized, meaning that each vehicle has its own camera and computing unit and communicates with nearby vehicles. Full article
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14 pages, 26592 KiB  
Article
Deep Learning-Based Multiple Droplet Contamination Detector for Vision Systems Using a You Only Look Once Algorithm
by Youngkwang Kim, Woochan Kim, Jungwoo Yoon, Sangkug Chung and Daegeun Kim
Information 2024, 15(3), 134; https://doi.org/10.3390/info15030134 - 28 Feb 2024
Viewed by 1993
Abstract
This paper presents a practical contamination detection system for camera lenses using image analysis with deep learning. The proposed system can detect contamination in camera digital images through contamination learning utilizing deep learning, and it aims to prevent performance degradation of intelligent vision [...] Read more.
This paper presents a practical contamination detection system for camera lenses using image analysis with deep learning. The proposed system can detect contamination in camera digital images through contamination learning utilizing deep learning, and it aims to prevent performance degradation of intelligent vision systems due to lens contamination in cameras. This system is based on the object detection algorithm YOLO (v5n, v5s, v5m, v5l, and v5x), which is trained with 4000 images captured under different lighting and background conditions. The trained models showed that the average precision improves as the algorithm size increases, especially for YOLOv5x, which showed excellent efficiency in detecting droplet contamination within 23 ms. They also achieved an average precision ([email protected]) of 87.46%, recall ([email protected]:0.95) of 51.90%, precision of 90.28%, recall of 81.47%, and F1 score of 85.64%. As a proof of concept, we demonstrated the identification and removal of contamination on camera lenses by integrating a contamination detection system and a transparent heater-based cleaning system. The proposed system is anticipated to be applied to autonomous driving systems, public safety surveillance cameras, environmental monitoring drones, etc., to increase operational safety and reliability. Full article
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15 pages, 2642 KiB  
Article
Why Does the Automation Say One Thing but Does Something Else? Effect of the Feedback Consistency and the Timing of Error on Trust in Automated Driving
by J. B. Manchon, Romane Beaufort, Mercedes Bueno and Jordan Navarro
Information 2022, 13(10), 480; https://doi.org/10.3390/info13100480 - 6 Oct 2022
Cited by 2 | Viewed by 1839
Abstract
Driving automation deeply modifies the role of the human operator behind the steering wheel. Trust is required for drivers to engage in such automation, and this trust also seems to be a determinant of drivers’ behaviors during automated drives. On the one hand, [...] Read more.
Driving automation deeply modifies the role of the human operator behind the steering wheel. Trust is required for drivers to engage in such automation, and this trust also seems to be a determinant of drivers’ behaviors during automated drives. On the one hand, first experiences with automation, either positive or not, are essential for drivers to calibrate their level of trust. On the other hand, an automation that provides feedback about its own level of capability to handle a specific driving situation may also help drivers to calibrate their level of trust. The reported experiment was undertaken to examine how the combination of these two effects will impact the driver trust calibration process. Four groups of drivers were randomly created. Each experienced either an early (i.e., directly after the beginning of the drive) or a late (i.e., directly before the end of it) critical situation that was poorly handled by the automation. In addition, they experienced either a consistent continuous feedback (i.e., that always correctly informed them about the situation), or an inconsistent one (i.e., that sometimes indicated dangers when there were none) during an automated drive in a driving simulator. Results showed the early- and poorly-handled critical situation had an enduring negative effect on drivers’ trust development compared to drivers who did not experience it. While being correctly understood, inconsistent feedback did not have an effect on trust during properly managed situations. These results suggest that the performance of the automation has the most severe influence on trust, and the automation’s feedback does not necessarily have the ability to influence drivers’ trust calibration during automated driving. Full article
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