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Sensors and Sensor Fusion for Decision Making for Autonomous Driving

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 8677

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Special Issue Editors

Dr. Eloisa Macedo

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1. TEMA - Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal
2. LASI—Intelligent Systems Associate Laboratory, 4800-058 Guimarães, Portugal
Interests: data-driven analysis; modelling driving behaviour; network performance; transport impacts
Special Issues, Collections and Topics in MDPI journals

Dr. Jorge Bandeira

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Guest Editor

1 TEMA - Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal
2. LASI—Intelligent Systems Associate Laboratory, 4800-058 Guimarães, Portugal
Interests: cooperative and intelligent transport systems; policy implications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid advances in technology have allowed us to witness the dawn of a new, transformative era in the road transport sector. The increasingly large circulation of connected and automated vehicles is becoming an increasingly certain and realizable fact, and furthermore a context in which technological developments and especially the foundations of machine learning and artificial intelligence are of great importance. Defining the road mobility landscape relies heavily on sensors and sensor fusion to perceive the environment, process data in real-time, and make safe and efficient decisions. Several emerging technologies allow vehicles to be sensitive to their surroundings, allowing for the collection of valuable information, but at the same time bringing with them challenges in how to use the information collected to improve the road landscape. In this Special Issue, we aim to reveal the transformative potential of sensory technology-driven systems, with a specific focus on improving mobility, increasing safety, reducing environmental impact and offering unprecedented levels of comfort to passengers and vulnerable road users. In short, our aim is to address the wider impact on traffic operations, urban infrastructures, environmental sustainability and social dynamics with the advancement of technology, while also focusing on aspects of scalability, as well as the cost–benefit ratio. We welcome and look forward to your contributions.

Dr. Eloisa Macedo
Dr. Jorge Bandeira
Guest Editors

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Keywords

mobility innovations
simulation and real-world testing
autonomous mobility solutions
driving behaviour
sustainable mobility

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Published Papers (4 papers)

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Research

29 pages, 4853 KB

Open AccessArticle

ROS 2-Based Architecture for Autonomous Driving Systems: Design and Implementation

by Andrea Bonci, Federico Brunella, Matteo Colletta, Alessandro Di Biase, Aldo Franco Dragoni and Angjelo Libofsha

Sensors 2026, 26(2), 463; https://doi.org/10.3390/s26020463 - 10 Jan 2026

Viewed by 3492

Abstract

Interest in the adoption of autonomous vehicles (AVs) continues to grow. It is essential to design new software architectures that meet stringent real-time, safety, and scalability requirements while integrating heterogeneous hardware and software solutions from different vendors and developers. This paper presents a lightweight, modular, and scalable architecture grounded in Service-Oriented Architecture (SOA) principles and implemented in ROS 2 (Robot Operating System 2). The proposed design leverages ROS 2’s Data Distribution System-based Quality-of-Service model to provide reliable communication, structured lifecycle management, and fault containment across distributed compute nodes. The architecture is organized into Perception, Planning, and Control layers with decoupled sensor access paths to satisfy heterogeneous frequency and hardware constraints. The decision-making core follows an event-driven policy that prioritizes fresh updates without enforcing global synchronization, applying zero-order hold where inputs are not refreshed. The architecture was validated on a 1:10-scale autonomous vehicle operating on a city-like track. The test environment covered canonical urban scenarios (lane-keeping, obstacle avoidance, traffic-sign recognition, intersections, overtaking, parking, and pedestrian interaction), with absolute positioning provided by an indoor GPS (Global Positioning System) localization setup. This work shows that the end-to-end Perception–Planning pipeline consistently met worst-case deadlines, yielding deterministic behaviour even under stress. The proposed architecture can be deemed compliant with real-time application standards for our use case on the 1:10 test vehicle, providing a robust foundation for deployment and further refinement. Full article

(This article belongs to the Special Issue Sensors and Sensor Fusion for Decision Making for Autonomous Driving)

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22 pages, 2616 KB

Open AccessArticle

Safety, Efficiency, and Mental Workload of Predictive Display in Simulated Teledriving

by Oren Musicant, Alexander Kuperman and Rotem Barachman

Sensors 2026, 26(1), 221; https://doi.org/10.3390/s26010221 - 29 Dec 2025

Viewed by 557

Abstract

Vehicle remote driving services are increasingly used in urban settings. Yet, vehicle-operator communication time delays may pose a challenge for teleoperators in maintaining safety and efficiency. The purpose of this study was to examine whether Predictive Displays (PDs), which show the vehicle’s predicted real-time position, improve performance, safety, and mental workload under moderate time delays typical of 4G/5G networks. Twenty-nine participants drove a simulated urban route containing pedestrian crossings, overtaking, gap acceptance, and traffic light challenges under three conditions: 50 ms delay (baseline), 150 ms delay without PD, and 150 ms delay with PD. We analyzed the counts of crashes and navigation errors, task completion times, and the probability and intensity of braking and steering events, as well as self-reports of workload and usability. Results indicate that though descriptive trends indicated slightly sharper steering and braking under the 150 ms time delay conditions, the 150 ms time delay did not significantly degrade performance or increase workload compared with the 50 ms baseline. In addition, the PD neither improved performance nor reduced workload. Overall, participants demonstrated tolerance to typical 4G/5G network time delays, leaving little room for improvement rendering the necessitating of PDs. Full article

(This article belongs to the Special Issue Sensors and Sensor Fusion for Decision Making for Autonomous Driving)

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23 pages, 1501 KB

Open AccessArticle

Improving Vehicle Connectivity Through a Novel Self-Organizing Network Mechanism

by Chia-Sheng Tsai and Chia-Kai Wen

Sensors 2025, 25(19), 6037; https://doi.org/10.3390/s25196037 - 1 Oct 2025

Viewed by 924

Abstract

A trend analysis mentioned that the global automotive Vehicle-to-Everything—also called V2X—market size will be reached at several billions in the near future. This information clearly highlights the importance of developing V2X communication. Nowadays, automobile manufacturers have introduced vehicles equipped with driver assistance and even conditional autonomous driving features. Light detection and ranging (LiDAR) components are used in sensor networks to detect objects around. Also, vehicles take advantage of LiDAR sensors to discover the neighbor cars in cognitive systems for road safety. Carrying on from our previous works, we found that organizing vehicles into groups can enhance the safety of the vehicle networks by LiDAR assistance. However, the success rate and reliability of grouping vehicles is an important issue. Also, enhancing existing Vehicle-to-Vehicle (V2V) communication mechanisms remains a key factor in ensuring that emergency messages can be transmitted both timely and accurately. To address this, in this research, a method is proposed to make vehicles on the road be self-organized well for Intelligent Transportation Systems (ITS). Also, we found that before data in each car is transmitted, the scenario that data is queued for waiting a random time exponentially distributed outperforms data being sent immediately. Full article

(This article belongs to the Special Issue Sensors and Sensor Fusion for Decision Making for Autonomous Driving)

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20 pages, 2072 KB

Open AccessArticle

Advancing Cognitive Load Detection in Simulated Driving Scenarios Through Deep Learning and fNIRS Data

by Mehshan Ahmed Khan, Houshyar Asadi, Mohammad Reza Chalak Qazani, Ghazal Bargshady, Sam Oladazimi, Thuong Hoang, Ghazal Rahimzadeh, Zoran Najdovski, Lei Wei, Hirash Moradi and Saeid Nahavandi

Sensors 2025, 25(16), 4921; https://doi.org/10.3390/s25164921 - 9 Aug 2025

Viewed by 3073

Abstract

The shift from manual to conditionally automated driving, supported by Advanced Driving Assistance Systems (ADASs), introduces challenges, particularly increased crash risks due to human factors like cognitive overload. Driving simulators provide a safe and controlled setting to study these human factors under complex conditions. This study leverages Functional Near-Infrared Spectroscopy (fNIRS) to dynamically assess cognitive load in a realistic driving simulator during a challenging night-time-rain scenario. Thirty-eight participants performed an auditory n-back task (0-, 1-, and 2-back) while driving, simulating multitasking demands. A sliding window approach was applied to the time-series fNIRS data to capture short-term fluctuations in brain activation. The data were analyzed using EEGNet, a deep learning model, with both overlapping and non-overlapping temporal segmentation strategies. Results revealed that classification performance is significantly influenced by the learning rate and windowing method. Notably, a learning rate of 0.001 yielded the highest performance, with 100% accuracy using overlapping windows and 97% accuracy with non-overlapping windows. These findings highlight the potential of combining fNIRS and deep learning for real-time cognitive load monitoring in simulated driving scenarios and demonstrate the importance of temporal modeling in physiological signal analysis. Full article

(This article belongs to the Special Issue Sensors and Sensor Fusion for Decision Making for Autonomous Driving)

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Sensors and Sensor Fusion for Decision Making for Autonomous Driving

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