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Sensors and Sensor Fusion in Autonomous Vehicles
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Sensors and Sensor Fusion in Autonomous Vehicles

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

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 10834

Special Issue Editors

Prof. Dr. Jianhua Ma

E-Mail Website
Guest Editor
Faculty of Computer and Information Science, Hosei University, 2 Chome-17-1 Fujimi, Chiyoda, Tokyo 102-8160, Japan
Interests: ubiquitous/pervasive computing and smart environment; u-Things, u-Intelligence and u-Science; cyber space, science and sociology; service and social computing; mobile multimedia and wireless network; IoT/iThings and Wisdom Web of Things (W2T); location and context-aware application; autonomic, trusted and ubisafe computing; hyperspace/hyperworld and cyber-I (digital colone)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qun Jin

E-Mail Website
Guest Editor
Department of Human Informatics and Cognitive Sciences, Faculty of Human Sciences, Waseda University, Tokyo 169-8050, Japan
Interests: behavior and cognitive informatics; big data; intelligence computing; blockchain; cyber security
Special Issues, Collections and Topics in MDPI journals
Dr. Gábor Kiss

E-Mail Website
Guest Editor
Institute of Mechanical Engineering and Security Science, Obuda University, H-1034 Budapest, Hungary
Interests: information security of self-driving vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In order to place autonomous cars slowly into our everyday lives from the world of science fiction, there was a need for new, or rather more accurate sensors and also a major development of artificial intelligence. This Special Issue of Sensors seeks the submission of review and original research articles related to sensors and sensor fusion in autonomous vehicles. Papers can focus on safety mistakes, security aggression, and achievable countermoves for self-driving vehicles, as well as Blockchain-based security attacks, flexible patterns for self-driving cars, and the STPA method and six-step model to integrate autonomous vehicle safety and security. Other research can give an overview of autonomous vehicle safety or study the cybersecurity of self-driving and remotely controlled transportation. The Special Issue is open to contributions dealing with many aspects of autonomous vehicle sensors and their fusion, such as multisensor fusion, big data processing for autonomous vehicles, sensor-related research, algorithms/technical development, and artificial intelligence methods for autonomous vehicle navigation.

Prof. Dr. Jianhua Ma
Prof. Dr. Qun Jin
Dr. Gábor Kiss
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

  • self-driving
  • autonomous vehicle
  • safety
  • security
  • big data
  • sensor
  • fusion
  • navigation

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Published Papers (5 papers)

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Research

16 pages, 2110 KiB  
Article
Fuzzy Petri Nets for Traffic Node Reliability
by Gabor Kiss and Peter Bakucz
Sensors 2024, 24(19), 6337; https://doi.org/10.3390/s24196337 - 30 Sep 2024
Viewed by 748
Abstract
Self-driving cars are one of the main areas of research today, but it has to be acknowledged that the information from the sensors (the perceptron) is a huge amount of data, which is now unmanageable even when projected onto a single traffic junction. [...] Read more.
Self-driving cars are one of the main areas of research today, but it has to be acknowledged that the information from the sensors (the perceptron) is a huge amount of data, which is now unmanageable even when projected onto a single traffic junction. In the case of self-driving, the nodes have to be sequenced and organized according to the planned route. A self-driving car in Hungary would have to be able to interpret more than 70,000 traffic junctions to be able to drive all over the country. Besides the huge amount of data, another problem is the issue of validation and verification. For self-driving cars, this implies a level of complexity using traditional methods that calls into question the economics of the already existing system. Fuzzy Petri nets provide an alternative solution to both problems. They allow us to obtain a model that accurately describes the reliability of a node through its dynamics, which is essential in perception since the more reliable a node is, the smaller the deep learning mesh required. In this paper, we outline the analysis of a traffic node’s safety using Petri nets and fuzzy analysis to gain information on the reliability of the node, which is essential for the modeling of self-driving cars, due to the deep learning model of perception. The reliability of the dynamics of the node is determined by using the modified fuzzy Petri net procedure. The need for a fuzzy extension of the Petri net was developed by knowledge of real traffic databases. Full article
(This article belongs to the Special Issue Sensors and Sensor Fusion in Autonomous Vehicles)
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27 pages, 2719 KiB  
Article
Comparison of KF-Based Vehicle Sideslip Estimation Logics with Increasing Complexity for a Passenger Car
by Lorenzo Ponticelli, Mario Barbaro, Geraldino Mandragora, Gianluca Pagano and Gonçalo Sousa Torres
Sensors 2024, 24(15), 4846; https://doi.org/10.3390/s24154846 - 25 Jul 2024
Cited by 1 | Viewed by 1030
Abstract
Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and [...] Read more.
Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers. Full article
(This article belongs to the Special Issue Sensors and Sensor Fusion in Autonomous Vehicles)
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19 pages, 7121 KiB  
Article
Sensor-Fused Nighttime System for Enhanced Pedestrian Detection in ADAS and Autonomous Vehicles
by Jungme Park, Bharath Kumar Thota and Karthik Somashekar
Sensors 2024, 24(14), 4755; https://doi.org/10.3390/s24144755 - 22 Jul 2024
Cited by 1 | Viewed by 1949
Abstract
Ensuring a safe nighttime environmental perception system relies on the early detection of vulnerable road users with minimal delay and high precision. This paper presents a sensor-fused nighttime environmental perception system by integrating data from thermal and RGB cameras. A new alignment algorithm [...] Read more.
Ensuring a safe nighttime environmental perception system relies on the early detection of vulnerable road users with minimal delay and high precision. This paper presents a sensor-fused nighttime environmental perception system by integrating data from thermal and RGB cameras. A new alignment algorithm is proposed to fuse the data from the two camera sensors. The proposed alignment procedure is crucial for effective sensor fusion. To develop a robust Deep Neural Network (DNN) system, nighttime thermal and RGB images were collected under various scenarios, creating a labeled dataset of 32,000 image pairs. Three fusion techniques were explored using transfer learning, alongside two single-sensor models using only RGB or thermal data. Five DNN models were developed and evaluated, with experimental results showing superior performance of fused models over non-fusion counterparts. The late-fusion system was selected for its optimal balance of accuracy and response time. For real-time inferencing, the best model was further optimized, achieving 33 fps on the embedded edge computing device, an 83.33% improvement in inference speed over the system without optimization. These findings are valuable for advancing Advanced Driver Assistance Systems (ADASs) and autonomous vehicle technologies, enhancing pedestrian detection during nighttime to improve road safety and reduce accidents. Full article
(This article belongs to the Special Issue Sensors and Sensor Fusion in Autonomous Vehicles)
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18 pages, 28847 KiB  
Article
Hierarchical Multi-Objective Optimization for Dedicated Bus Punctuality and Supply–Demand Balance Control
by Chunlin Shang, Fenghua Zhu, Yancai Xu, Xiaoming Liu and Tianhua Jiang
Sensors 2023, 23(9), 4552; https://doi.org/10.3390/s23094552 - 7 May 2023
Cited by 4 | Viewed by 2209
Abstract
Public transportation is a crucial component of urban transportation systems, and improving passenger sharing rates can help alleviate traffic congestion. To enhance the punctuality and supply–demand balance of dedicated buses, we propose a hierarchical multi-objective optimization model to optimize bus guidance speeds and [...] Read more.
Public transportation is a crucial component of urban transportation systems, and improving passenger sharing rates can help alleviate traffic congestion. To enhance the punctuality and supply–demand balance of dedicated buses, we propose a hierarchical multi-objective optimization model to optimize bus guidance speeds and bus operation schedules. Firstly, we present an intelligent decision-making method for bus driving speed based on the mathematical description of bus operation states and the application of the Lagrange multiplier method, which improves the overall punctuality rate of the bus line. Secondly, we propose an optimization method for bus operation schedules that respond to passenger needs by optimizing departure time intervals and station schedules for supply–demand balance. The experiments were conducted in Future Science City, Beijing, China. The results show that the bus line’s punctuality rate has increased to 90.53%, while the retention rate for platform passengers and the intersection stop rate have decreased by 36.22% and 60.93%, respectively. These findings verify the effectiveness and practicality of the proposed hierarchical multi-objective optimization model. Full article
(This article belongs to the Special Issue Sensors and Sensor Fusion in Autonomous Vehicles)
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28 pages, 1957 KiB  
Article
Information Fusion in Autonomous Vehicle Using Artificial Neural Group Key Synchronization
by Mohammad Zubair Khan, Arindam Sarkar, Hamza Ghandorh, Maha Driss and Wadii Boulila
Sensors 2022, 22(4), 1652; https://doi.org/10.3390/s22041652 - 20 Feb 2022
Cited by 8 | Viewed by 3182
Abstract
Information fusion in automated vehicle for various datatypes emanating from many resources is the foundation for making choices in intelligent transportation autonomous cars. To facilitate data sharing, a variety of communication methods have been integrated to build a diverse V2X infrastructure. However, information [...] Read more.
Information fusion in automated vehicle for various datatypes emanating from many resources is the foundation for making choices in intelligent transportation autonomous cars. To facilitate data sharing, a variety of communication methods have been integrated to build a diverse V2X infrastructure. However, information fusion security frameworks are currently intended for specific application instances, that are insufficient to fulfill the overall requirements of Mutual Intelligent Transportation Systems (MITS). In this work, a data fusion security infrastructure has been developed with varying degrees of trust. Furthermore, in the V2X heterogeneous networks, this paper offers an efficient and effective information fusion security mechanism for multiple sources and multiple type data sharing. An area-based PKI architecture with speed provided by a Graphic Processing Unit (GPU) is given in especially for artificial neural synchronization-based quick group key exchange. A parametric test is performed to ensure that the proposed data fusion trust solution meets the stringent delay requirements of V2X systems. The efficiency of the suggested method is tested, and the results show that it surpasses similar strategies already in use. Full article
(This article belongs to the Special Issue Sensors and Sensor Fusion in Autonomous Vehicles)
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