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Automated Driving Systems Design for the Improvement of Safety and Comfort in Automotive Vehicles

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

Deadline for manuscript submissions: closed (12 August 2024) | Viewed by 6149

Special Issue Editors

Dr. Fernando Viadero-Monasterio

E-Mail Website
Guest Editor
Mechanical Engineering Department, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain
Interests: advanced driver assistance systems; mechanical stability; road vehicles; uncertain systems; vehicle dynamics
Prof. Dr. Beatriz L. Boada

E-Mail Website
Guest Editor
Mechanical Engineering Department, Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain
Interests: vehicle control; vehicle safety; Internet of things; sensor fusion; intelligent vehicles
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maria Jesús López Boada

E-Mail Website
Guest Editor
Mechanical Engineering Department, Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Madrid, Spain
Interests: vehicle control; vehicle safety; internet of things; sensor fusion; intelligent vehicles; neural networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Automated driving systems (ADS) have emerged as a transformative technology with the potential to revolutionize transportation. These systems, often referred to as self-driving cars or autonomous vehicles, rely on advanced sensors, artificial intelligence, and connectivity to navigate and operate vehicles without human intervention. The importance of ADS lies in the numerous benefits these offer, including enhanced safety, increased efficiency, and improved accessibility. One of the primary advantages of ADS is their potential to significantly reduce traffic accidents. Human error is responsible for the majority of road accidents, and automated systems have the potential to eliminate or greatly minimize these errors. With their ability to sense the environment and react faster than humans, ADS can detect and respond to potential hazards more effectively, thus reducing the risk of collisions. Additionally, ADS can optimize traffic flow and reduce congestion by efficiently coordinating vehicle movements. Through real-time data analysis and communication between vehicles, autonomous systems can adjust speed, route, and spacing to ensure smoother traffic flow. This improved efficiency not only saves time for individuals but also reduces fuel consumption and lowers carbon emissions, contributing to a greener and more sustainable future. However, despite the significant potential benefits, the widespread adoption of ADS also presents several challenges. One of the major hurdles is ensuring the safety and reliability of these systems. Autonomous vehicles must be capable of navigating complex and unpredictable scenarios, including adverse weather conditions, construction zones, and interactions with pedestrians and human-driven vehicles. Developing robust algorithms and conducting extensive testing are crucial to ensure the safe operation of ADS.

Dr. Fernando Viadero-Monasterio
Prof. Dr. Beatriz L. Boada
Prof. Dr. Maria Jesús López Boada
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • automated vehicles
  • artificial intelligence
  • path tracking
  • human–machine shared control
  • platoon control
  • V2V and V2I communication
  • safety
  • comfort
  • robust control
  • vehicle dynamics

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Published Papers (3 papers)

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Research

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22 pages, 3449 KiB  
Article
fBrake, a Method to Simulate the Brake Efficiency of Laden Light Passenger Vehicles in PTIs While Measuring the Braking Forces of Their Unladen Configurations
by Víctor Romero-Gómez and José Luis San Román
Sensors 2024, 24(20), 6602; https://doi.org/10.3390/s24206602 - 13 Oct 2024
Viewed by 657
Abstract
This study introduces fBrake, a novel simulation method now designed for use in periodic technical inspections of M1 and N1 vehicle categories, addressing challenges posed by Directive 2014/45/EU. The directive mandates that braking efficiency must be measured relative to the vehicle’s [...] Read more.
This study introduces fBrake, a novel simulation method now designed for use in periodic technical inspections of M1 and N1 vehicle categories, addressing challenges posed by Directive 2014/45/EU. The directive mandates that braking efficiency must be measured relative to the vehicle’s maximum mass, which often results in underperformance during inspections due to vehicles typically being unladen. This discrepancy arises because the maximum braking forces are proportional to the vertical load on the wheels, causing empty vehicles to lock their wheels prematurely compared to laden ones. fBrake simulates the braking forces of unladen vehicles to reflect a laden state by employing an optimal brake-force distribution curve that aligns with the vehicle’s inherent braking behavior, whether through proportioning valves or through electronic brake distribution systems in anti-lock-braking-system-equipped vehicles. Our methodology, previously applied to heavy vehicles, involved extensive experimentation with a roller brake tester, comparing the actual braking performances of dozens of vehicles to those of their simulated counterparts using fBrake. The results demonstrate that fBrake reliably replicates the braking efficiency of laden vehicles, validating its use as an accurate and effective tool for braking system assessments in periodic inspections, irrespective of the vehicle’s load condition during the test. This approach ensures compliance with regulatory requirements while enhancing the reliability and safety of vehicle inspections. Full article
(This article belongs to the Special Issue Automated Driving Systems Design for the Improvement of Safety and Comfort in Automotive Vehicles)
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24 pages, 7779 KiB  
Article
A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences
by Jiachen Chen, Hui Chen, Xiaoming Lan, Bin Zhong and Wei Ran
Sensors 2024, 24(5), 1666; https://doi.org/10.3390/s24051666 - 4 Mar 2024
Cited by 1 | Viewed by 1389
Abstract
To satisfy the preference of each driver, the development of a Lane-Keeping Assistance (LKA) system that can adapt to individual drivers has become a research hotspot in recent years. However, existing studies have mostly relied on the assumption that the LKA characteristic aligned [...] Read more.
To satisfy the preference of each driver, the development of a Lane-Keeping Assistance (LKA) system that can adapt to individual drivers has become a research hotspot in recent years. However, existing studies have mostly relied on the assumption that the LKA characteristic aligned with the driver’s preference is consistent with this driver’s naturalistic driving characteristic. Nevertheless, this assumption may not always hold true, causing limitations to the effectiveness of this method. This paper proposes a novel method for a Driver-Adaptive Lane-Keeping Assistance (DALKA) system based on drivers’ real preferences. First, metrics are extracted from collected naturalistic driving data using action point theory to describe drivers’ naturalistic driving characteristics. Then, the subjective and objective evaluation method is introduced to obtain the real preference of each test driver for the LKA system. Finally, machine learning methods are employed to train a model that relates naturalistic driving characteristics to the drivers’ real preferences, and the model-predicted preferences are integrated into the DALKA system. The developed DALKA system is then subjectively evaluated by the drivers. The results show that our DALKA system, developed using this method, can enhance or maintain the subjective evaluations of the LKA system for most drivers. Full article
(This article belongs to the Special Issue Automated Driving Systems Design for the Improvement of Safety and Comfort in Automotive Vehicles)
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Review

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21 pages, 1029 KiB  
Review
Automotive Cybersecurity: A Survey on Frameworks, Standards, and Testing and Monitoring Technologies
by Claudiu Vasile Kifor and Aurelian Popescu
Sensors 2024, 24(18), 6139; https://doi.org/10.3390/s24186139 - 23 Sep 2024
Viewed by 3057
Abstract
Modern vehicles are increasingly interconnected through various communication channels, which requires secure access for authorized users, the protection of driver assistance and autonomous driving system data, and the assurance of data integrity against misuse or manipulation. While these advancements offer numerous benefits, recent [...] Read more.
Modern vehicles are increasingly interconnected through various communication channels, which requires secure access for authorized users, the protection of driver assistance and autonomous driving system data, and the assurance of data integrity against misuse or manipulation. While these advancements offer numerous benefits, recent years have exposed many intrusion incidents, revealing vulnerabilities and weaknesses in current systems. To sustain and enhance the performance, quality, and reliability of vehicle systems, software engineers face significant challenges, including in diverse communication channels, software integration, complex testing, compatibility, core reusability, safety and reliability assurance, data privacy, and software security. Addressing cybersecurity risks presents a substantial challenge in finding practical solutions to these issues. This study aims to analyze the current state of research regarding automotive cybersecurity, with a particular focus on four main themes: frameworks and technologies, standards and regulations, monitoring and vulnerability management, and testing and validation. This paper highlights key findings, identifies existing research gaps, and proposes directions for future research that will be useful for both researchers and practitioners. Full article
(This article belongs to the Special Issue Automated Driving Systems Design for the Improvement of Safety and Comfort in Automotive Vehicles)
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