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Vehicular Sensing for Improved Urban Mobility

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

Deadline for manuscript submissions: 20 May 2024 | Viewed by 12722

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Special Issue Editors

Prof. Dr. Constantin Caruntu

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Guest Editor

Department of Automatic Control and Applied Informatics, Gheorghe Asachi Technical University of Iasi, Str. Prof. D. Mangeron, No. 26, 700050 Iasi, Romania
Interests: model predictive control; networked/distributed control systems; automotive control systems; vehicle dynamics and control; cooperative systems; connected and automated mobility; vehicle connectivity; 5G applications
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Dr. Ciprian Romeo Comşa

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Guest Editor

Department of Telecommunications and Information Technologies, Gheorghe Asachi Technical University of Iasi (TUIASI), 700506 Iasi, Romania
Interests: innovation management; radio localization and signal processing for wireless communications; vehicular communications and sensing; artificial intelligence applied in the fields of automotive and communications; 5G and 6G technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last couple of years, technology advancements in the automotive industry have experienced a tremendous leap toward connected and autonomous vehicles (CAVs). However, although there is still much progress ahead to meet the full safety and security requirements for completely autonomous vehicles, the opportunity of developing safety applications for traffic participants has emerged by considering data from their own environmental sensors and using connectivity with other traffic participants or smart infrastructure systems, usually encountered in urban areas.

The role of environmental sensors, e.g., cameras, radars, lidars, is to provide comprehensive information about the objects around the vehicle. Moreover, the latest developments in the area of CAVs have also led to the possibility of sharing information regarding the surroundings between traffic participants and even with smart infrastructure, called collective perception. While autonomous vehicles create an environment model (EM) of their own with the support of data from sensors that equip the vehicle, such a model can be improved to create a comprehensive EM with information that could be hardly perceived or even totally unperceivable from their perspective thanks to advancements in vehicle-to-everything (V2X) communication technologies. Thus, vehicles transmit to other traffic participants and infrastructure information about their heading, position, and speed through cooperative awareness messages (CAMs), while information from sensors used to increase precision and accuracy is transmitted through collective perception messages (CPMs).

Furthermore, urban roadside infrastructure can play a decisive part in improving the safety of traffic participants, but it needs to be embedded with various sensors, e.g., cameras, radars, lidars, ultra-wideband (UWB) sensors, or GNSS sensors, for passive awareness. To improve traffic participants’ awareness through the passive approach, smart infrastructure uses state-of-the-art tools such as computer vision and artificial intelligence (AI), which play an important role in object detection and classification, pose estimation, object tracking, and behavior prediction for all traffic participants.

This Special Issue welcomes contributions dealing with all the technological facets of vehicular sensing in the context of urban mobility, including architecture, emerging sensors, communication technologies, and advanced applications, sensing, and algorithms, but also on deployment issues, such as the development of smart infrastructure systems used to gather information from vehicles and to share safety-critical information.

The topics of interest include but are not limited to the following:

Intelligent transportation systems;
Intelligent vehicles;
Connected and autonomous vehicles;
Urban mobility;
Smart infrastructure;
Vehicular ad hoc networks (VANETs);
Vehicle communications: V2X, V2V, V2I, 5G;
Artificial intelligence in automated vehicles, e.g., self-driving car;
Cyberphysical system control and safety in vehicular networks.

Prof. Dr. Constantin Caruntu
Dr. Ciprian Romeo Comşa
Guest Editors

Manuscript Submission Information

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Keywords

intelligent transportation systems
intelligent vehicles
connected and autonomous vehicles
urban mobility
smart infrastructure
vehicular ad hoc networks (VANETs)
vehicle communications: V2X, V2V, V2I, 5G
artificial intelligence in automated vehicles, e.g., self-driving car
cyberphysical system control and safety in vehicular networks

Published Papers (9 papers)

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Research

14 pages, 2946 KiB

Open AccessArticle

The Impact of Line-of-Sight and Connected Vehicle Technology on Mitigating and Preventing Crash and Near-Crash Events

by Eileen Herbers, Zachary Doerzaph and Loren Stowe

Sensors 2024, 24(2), 484; https://doi.org/10.3390/s24020484 - 12 Jan 2024

Viewed by 489

Abstract

Line-of-sight (LOS) sensors developed in newer vehicles have the potential to help avoid crash and near-crash scenarios with advanced driving-assistance systems; furthermore, connected vehicle technologies (CVT) also have a promising role in advancing vehicle safety. This study used crash and near-crash events from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) to reconstruct crash events so that the applicable benefit of sensors in LOS systems and CVT can be compared. The benefits of CVT over LOS systems include additional reaction time before a predicted crash, as well as a lower deceleration value needed to prevent a crash. This work acts as a baseline effort to determine the potential safety benefits of CVT-enabled systems over LOS sensors alone. Full article

(This article belongs to the Special Issue Vehicular Sensing for Improved Urban Mobility)

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16 pages, 16388 KiB

Open AccessArticle

Improving Turn Movement Count Using Cooperative Feedback

by Patrick Heyer-Wollenberg, Chengjin Lyu, Ljubomir Jovanov, Bart Goossens and Wilfried Philips

Sensors 2023, 23(24), 9772; https://doi.org/10.3390/s23249772 - 12 Dec 2023

Viewed by 518

Abstract

In this paper, we propose a new cooperative method that improves the accuracy of Turn Movement Count (TMC) under challenging conditions by introducing contextual observations from the surrounding areas. The proposed method focuses on the correct identification of the movements in conditions where current methods have difficulties. Existing vision-based TMC systems are limited under heavy traffic conditions. The main problems for most existing methods are occlusions between vehicles that prevent the correct detection and tracking of the vehicles through the entire intersection and the assessment of the vehicle’s entry and exit points, incorrectly assigning the movement. The proposed method intends to overcome this incapability by sharing information with other observation systems located at neighboring intersections. Shared information is used in a cooperative scheme to infer the missing data, thereby improving the assessment that would otherwise not be counted or miscounted. Experimental evaluation of the system shows a clear improvement over related reference methods. Full article

(This article belongs to the Special Issue Vehicular Sensing for Improved Urban Mobility)

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23 pages, 2852 KiB

Open AccessArticle

Urban Advanced Mobility Dependability: A Model-Based Quantification on Vehicular Ad Hoc Networks with Virtual Machine Migration

by Luis Guilherme Silva, Israel Cardoso, Carlos Brito, Vandirleya Barbosa, Bruno Nogueira, Eunmi Choi, Tuan Anh Nguyen, Dugki Min, Jae Woo Lee and Francisco Airton Silva

Sensors 2023, 23(23), 9485; https://doi.org/10.3390/s23239485 - 28 Nov 2023

Viewed by 691

Abstract

In the rapidly evolving urban advanced mobility (UAM) sphere, Vehicular Ad Hoc Networks (VANETs) are crucial for robust communication and operational efficiency in future urban environments. This paper quantifies VANETs to improve their reliability and availability, essential for integrating UAM into urban infrastructures. It proposes a novel Stochastic Petri Nets (SPN) method for evaluating VANET-based Vehicle Communication and Control (VCC) architectures, crucial given the dynamic demands of UAM. The SPN model, incorporating virtual machine (VM) migration and Edge Computing, addresses VANET integration challenges with Edge Computing. It uses stochastic elements to mirror VANET scenarios, enhancing network robustness and dependability, vital for the operational integrity of UAM. Case studies using this model offer insights into system availability and reliability, guiding VANET optimizations for UAM. The paper also applies a Design of Experiments (DoE) approach for a sensitivity analysis of SPN components, identifying key parameters affecting system availability. This is critical for refining the model for UAM efficiency. This research is significant for monitoring UAM systems in future cities, presenting a cost-effective framework over traditional methods and advancing VANET reliability and availability in urban mobility contexts. Full article

(This article belongs to the Special Issue Vehicular Sensing for Improved Urban Mobility)

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28 pages, 4493 KiB

Open AccessArticle

Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication

by Razvan-Gabriel Lazar, Ovidiu Pauca, Anca Maxim and Constantin-Florin Caruntu

Sensors 2023, 23(17), 7576; https://doi.org/10.3390/s23177576 - 31 Aug 2023

Cited by 2 | Viewed by 1269

Abstract

A suitable control architecture for connected vehicle platoons may be seen as a promising solution for today’s traffic problems, by improving road safety and traffic flow, reducing emissions and fuel consumption, and increasing driver comfort. This paper provides a comprehensive overview concerning the defining levels of a general control architecture for connected vehicle platoons, intending to illustrate the options available in terms of sensor technologies, in-vehicle networks, vehicular communication, and control solutions. Moreover, starting from the proposed control architecture, a solution that implements a Cooperative Adaptive Cruise Control (CACC) functionality for a vehicle platoon is designed. Also, two control algorithms based on the distributed model-based predictive control (DMPC) strategy and the feedback gain matrix method for the control level of the CACC functionality are proposed. The designed architecture was tested in a simulation scenario, and the obtained results show the control performances achieved using the proposed solutions suitable for the longitudinal dynamics of vehicle platoons. Full article

(This article belongs to the Special Issue Vehicular Sensing for Improved Urban Mobility)

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15 pages, 20924 KiB

Open AccessArticle

High-Dynamic-Range Tone Mapping in Intelligent Automotive Systems

by Ivana Shopovska, Ana Stojkovic, Jan Aelterman, David Van Hamme and Wilfried Philips

Sensors 2023, 23(12), 5767; https://doi.org/10.3390/s23125767 - 20 Jun 2023

Cited by 2 | Viewed by 1694

Abstract

Intelligent driver assistance systems are becoming increasingly popular in modern passenger vehicles. A crucial component of intelligent vehicles is the ability to detect vulnerable road users (VRUs) for an early and safe response. However, standard imaging sensors perform poorly in conditions of strong illumination contrast, such as approaching a tunnel or at night, due to their dynamic range limitations. In this paper, we focus on the use of high-dynamic-range (HDR) imaging sensors in vehicle perception systems and the subsequent need for tone mapping of the acquired data into a standard 8-bit representation. To our knowledge, no previous studies have evaluated the impact of tone mapping on object detection performance. We investigate the potential for optimizing HDR tone mapping to achieve a natural image appearance while facilitating object detection of state-of-the-art detectors designed for standard dynamic range (SDR) images. Our proposed approach relies on a lightweight convolutional neural network (CNN) that tone maps HDR video frames into a standard 8-bit representation. We introduce a novel training approach called detection-informed tone mapping (DI-TM) and evaluate its performance with respect to its effectiveness and robustness in various scene conditions, as well as its performance relative to an existing state-of-the-art tone mapping method. The results show that the proposed DI-TM method achieves the best results in terms of detection performance metrics in challenging dynamic range conditions, while both methods perform well in typical, non-challenging conditions. In challenging conditions, our method improves the detection F2 score by 13%. Compared to SDR images, the increase in

F_{2}

score is 49%. Full article

(This article belongs to the Special Issue Vehicular Sensing for Improved Urban Mobility)

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21 pages, 5824 KiB

Open AccessArticle

Model-Predictive Control for Omnidirectional Mobile Robots in Logistic Environments Based on Object Detection Using CNNs

by Stefan-Daniel Achirei, Razvan Mocanu, Alexandru-Tudor Popovici and Constantin-Catalin Dosoftei

Sensors 2023, 23(11), 4992; https://doi.org/10.3390/s23114992 - 23 May 2023

Cited by 5 | Viewed by 1716

Abstract

Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion control algorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictive controller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictive control approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot. Full article

(This article belongs to the Special Issue Vehicular Sensing for Improved Urban Mobility)

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17 pages, 3242 KiB

Open AccessArticle

Experimental Study on Longitudinal Acceleration of Urban Buses and Coaches in Different Road Maneuvers

by Damian Frej, Paweł Grabski, Rafał S. Jurecki and Emilia M. Szumska

Sensors 2023, 23(6), 3125; https://doi.org/10.3390/s23063125 - 15 Mar 2023

Cited by 2 | Viewed by 1712

Abstract

A vehicle’s longitudinal acceleration is a parameter often used for determining vehicle motion dynamics. This parameter can also be used to evaluate driver behavior and passenger comfort analysis. The paper presents the results of longitudinal acceleration tests of city buses and coaches recorded during rapid acceleration and braking maneuvers. The presented test results demonstrate that longitudinal acceleration is significantly affected by road conditions and surface type. In addition, the paper presents the values of longitudinal accelerations of city buses and coaches during their regular operation. These results were obtained on the basis of registration of vehicle traffic parameters in a continuous and long-term manner. The test results showed that the maximum deceleration values recorded during the tests of city buses and coaches in real traffic conditions were much lower than the maximum deceleration values found during sudden braking maneuvers. This proves that the tested drivers in real conditions did not have to use sudden braking. The maximum positive acceleration values recorded in acceleration maneuvers were slightly higher than the acceleration values logged during the rapid acceleration tests on the track. Full article

(This article belongs to the Special Issue Vehicular Sensing for Improved Urban Mobility)

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25 pages, 1153 KiB

Open AccessArticle

Provably Secure Mutual Authentication and Key Agreement Scheme Using PUF in Internet of Drones Deployments

by Yohan Park, Daeun Ryu, Deokkyu Kwon and Youngho Park

Sensors 2023, 23(4), 2034; https://doi.org/10.3390/s23042034 - 10 Feb 2023

Cited by 5 | Viewed by 1952

Abstract

Internet of Drones (IoD), designed to coordinate the access of unmanned aerial vehicles (UAVs), is a specific application of the Internet of Things (IoT). Drones are used to control airspace and offer services such as rescue, traffic surveillance, environmental monitoring, delivery and so on. However, IoD continues to suffer from privacy and security issues. Firstly, messages are transmitted over public channels in IoD environments, which compromises data security. Further, sensitive data can also be extracted from stolen mobile devices of remote users. Moreover, drones are susceptible to physical capture and manipulation by adversaries, which are called drone capture attacks. Thus, the development of a secure and lightweight authentication scheme is essential to overcoming these security vulnerabilities, even on resource-constrained drones. In 2021, Akram et al. proposed a secure and lightweight user–drone authentication scheme for drone networks. However, we discovered that Akram et al.’s scheme is susceptible to user and drone impersonation, verification table leakage, and denial of service (DoS) attacks. Furthermore, their scheme cannot provide perfect forward secrecy. To overcome the aforementioned security vulnerabilities, we propose a secure mutual authentication and key agreement scheme between user and drone pairs. The proposed scheme utilizes physical unclonable function (PUF) to give drones uniqueness and resistance against drone stolen attacks. Moreover, the proposed scheme uses a fuzzy extractor to utilize the biometrics of users as secret parameters. We analyze the security of the proposed scheme using informal security analysis, Burrows–Abadi–Needham (BAN) logic, a Real-or-Random (RoR) model, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation. We also compared the security features and performance of the proposed scheme and the existing related schemes. Therefore, we demonstrate that the proposed scheme is suitable for IoD environments that can provide users with secure and convenient wireless communications. Full article

(This article belongs to the Special Issue Vehicular Sensing for Improved Urban Mobility)

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16 pages, 5163 KiB

Open AccessArticle

An Algorithm for Online Stochastic Error Modeling of Inertial Sensors in Urban Cities

by Luodi Zhao and Long Zhao

Sensors 2023, 23(3), 1257; https://doi.org/10.3390/s23031257 - 21 Jan 2023

Cited by 5 | Viewed by 1501

Abstract

Regardless of whether the global navigation satellite system (GNSS)/inertial navigation system (INS) is integrated or the INS operates independently during GNSS outages, the stochastic error of the inertial sensor has an important impact on the navigation performance. The structure of stochastic error in low-cost inertial sensors is quite complex; therefore, it is difficult to identify and separate errors in the spectral domain using classical stochastic error methods such as the Allan variance (AV) method and power spectral density (PSD) analysis. However, a recently proposed estimation, based on generalized wavelet moment estimation (GMWM), is applied to the stochastic error modeling of inertial sensors, giving significant advantages. Focusing on the online implementation of GMWM and its integration within a general navigation filter, this paper proposes an algorithm for online stochastic error calibration of inertial sensors in urban cities. We further develop the autonomous stochastic error model by constructing a complete stochastic error model and determining model ranking criterion. Then, a detecting module is designed to work together with the autonomous stochastic error model as feedback for the INS/GNSS integration. Finally, two experiments are conducted to compare the positioning performance of this algorithm with other classical methods. The results validate the capability of this algorithm to improve navigation accuracy and achieve the online realization of complex stochastic models. Full article

(This article belongs to the Special Issue Vehicular Sensing for Improved Urban Mobility)

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