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

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 12460

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Dr. Tomás Mateo Sanguino

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Department of Electronic Engineering, Computer Systems and Automatics, University of Huelva, Av. de las Artes s/n, 21007 Huelva, Spain
Interests: road safety; communications; cybersecurity; smart city
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Special Issue Information

Dear Colleagues,

Intelligent transportation systems (ITSs) provide leading-edge information and communication technologies used in transportation and traffic management systems to improve the safety, efficiency, and sustainability of transportation networks; reduce traffic congestion; and enhance drivers' experiences. With the ITS playing an important role in our daily life, academics and industry are increasingly focusing on intelligent transportation systems (ITSs) to improve the performance of transportation systems.

It is anticipated that future technologies, such as the intelligent transportation system (ITS) and automated vehicles, will revolutionize how people commute in smart cities. It is possible to decrease traffic congestion, improve mobility and logistics flows, and even improve air quality using ITS and autonomous vehicles (whether terrestrial or aerial). Therefore, it is essential that we gain further knowledge in this research field and develop the necessary planning and policies for a smart city approach.

This Special Issue aims to collate original research and review articles that discuss sustainable mobility in alternative transport systems. Potential topics include (but are not limited to) the following:

connected technologies and autonomous driving;
the integration of vehicle communication;
sensor technologies for intelligent transportation systems;
IoT in shared mobility routings;
sustainable and smart transport solutions;
transport and traffic safety challenges.

Dr. Tomás Mateo Sanguino
Guest Editor

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Published Papers (7 papers)

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Research

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18 pages, 1439 KiB

Open AccessArticle

Energy Efficiency Optimisation of Joint Computational Task Offloading and Resource Allocation Using Particle Swarm Optimisation Approach in Vehicular Edge Networks

by Amjad Alam, Purav Shah, Ramona Trestian, Kamran Ali and Glenford Mapp

Sensors 2024, 24(10), 3001; https://doi.org/10.3390/s24103001 - 9 May 2024

Viewed by 961

Abstract

With the progression of smart vehicles, i.e., connected autonomous vehicles (CAVs), and wireless technologies, there has been an increased need for substantial computational operations for tasks such as path planning, scene recognition, and vision-based object detection. Managing these intensive computational applications is concerned with significant energy consumption. Hence, for this article, a low-cost and sustainable solution using computational offloading and efficient resource allocation at edge devices within the Internet of Vehicles (IoV) framework has been utilised. To address the quality of service (QoS) among vehicles, a trade-off between energy consumption and computational time has been taken into consideration while deciding on the offloading process and resource allocation. The offloading process has been assigned at a minimum wireless resource block level to adapt to the beyond 5G (B5G) network. The novel approach of joint optimisation of computational resources and task offloading decisions uses the meta-heuristic particle swarm optimisation (PSO) algorithm and decision analysis (DA) to find the near-optimal solution. Subsequently, a comparison is made with other proposed algorithms, namely CTORA, CODO, and Heuristics, in terms of computational efficiency and latency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating an 8% and a 5% increase in energy efficiency. Full article

(This article belongs to the Special Issue Sustainable Intelligent and Connected Transportation)

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22 pages, 1239 KiB

Open AccessArticle

Anonymous Traffic Detection Based on Feature Engineering and Reinforcement Learning

by Dazhou Liu and Younghee Park

Sensors 2024, 24(7), 2295; https://doi.org/10.3390/s24072295 - 4 Apr 2024

Cited by 1 | Viewed by 1049

Abstract

Anonymous networks, which aim primarily to protect user identities, have gained prominence as tools for enhancing network security and anonymity. Nonetheless, these networks have become a platform for adversarial affairs and sources of suspicious attack traffic. To defend against unpredictable adversaries on the Internet, detecting anonymous network traffic has emerged as a necessity. Many supervised approaches to identify anonymous traffic have harnessed machine learning strategies. However, many require access to engineered datasets and complex architectures to extract the desired information. Due to the resistance of anonymous network traffic to traffic analysis and the scarcity of publicly available datasets, those approaches may need to improve their training efficiency and achieve a higher performance when it comes to anonymous traffic detection. This study utilizes feature engineering techniques to extract pattern information and rank the feature importance of the static traces of anonymous traffic. To leverage these pattern attributes effectively, we developed a reinforcement learning framework that encompasses four key components: states, actions, rewards, and state transitions. A lightweight system is devised to classify anonymous and non-anonymous network traffic. Subsequently, two fine-tuned thresholds are proposed to substitute the traditional labels in a binary classification system. The system will identify anonymous network traffic without reliance on labeled data. The experimental results underscore that the system can identify anonymous traffic with an accuracy rate exceeding 80% (when based on pattern information). Full article

(This article belongs to the Special Issue Sustainable Intelligent and Connected Transportation)

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25 pages, 13412 KiB

Open AccessArticle

Exploring Safety–Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts

by Yu Wei and Xiaozheng He

Sensors 2024, 24(5), 1614; https://doi.org/10.3390/s24051614 - 1 Mar 2024

Viewed by 998

Abstract

Advanced sensing technologies and communication capabilities of Connected and Autonomous Vehicles (CAVs) empower them to capture the dynamics of surrounding vehicles, including speeds and positions of those behind, enabling judicious responsive maneuvers. The acquired dynamics information of vehicles spurred the development of various cooperative platoon controls, particularly designed to enhance platoon stability with reduced spacing for reliable roadway capacity increase. These controls leverage abundant information transmitted through various communication topologies. Despite these advancements, the impact of different vehicle dynamics information on platoon safety remains underexplored, as current research predominantly focuses on stability analysis. This knowledge gap highlights the critical need for further investigation into how diverse vehicle dynamics information influences platoon safety. To address this gap, this research introduces a novel framework based on the concept of phase shift, aiming to scrutinize the tradeoffs between the safety and stability of CAV platoons formed upon bidirectional information flow topology. Our investigation focuses on platoon controls built upon bidirectional information flow topologies using diverse dynamics information of vehicles. Our research findings emphasize that the integration of various types of information into CAV platoon controls does not universally yield benefits. Specifically, incorporating spacing information can enhance both platoon safety and string stability. In contrast, velocity difference information can improve either safety or string stability, but not both simultaneously. These findings offer valuable insights into the formulation of CAV platoon control principles built upon diverse communication topologies. This research contributes a nuanced understanding of the intricate interplay between safety and stability in CAV platoons, emphasizing the importance of information dynamics in shaping effective control strategies. Full article

(This article belongs to the Special Issue Sustainable Intelligent and Connected Transportation)

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25 pages, 3116 KiB

Open AccessArticle

Online Quantitative Analysis of Perception Uncertainty Based on High-Definition Map

by Mingliang Yang, Xinyu Jiao, Kun Jiang, Qian Cheng, Yanding Yang, Mengmeng Yang and Diange Yang

Sensors 2023, 23(24), 9876; https://doi.org/10.3390/s23249876 - 17 Dec 2023

Viewed by 1309

Abstract

Environmental perception plays a fundamental role in decision-making and is crucial for ensuring the safety of autonomous driving. A pressing challenge is the online evaluation of perception uncertainty, a crucial step towards ensuring the safety and the industrialization of autonomous driving. High-definition maps offer precise information about static elements on the road, along with their topological relationships. As a result, the map can provide valuable prior information for assessing the uncertainty associated with static elements. In this paper, a method for evaluating perception uncertainty online, encompassing both static and dynamic elements, is introduced based on the high-definition map. The proposed method is as follows: Firstly, the uncertainty of static elements in perception, including the uncertainty of their existence and spatial information, was assessed based on the spatial and topological features of the static environmental elements; secondly, an online assessment model for the uncertainty of dynamic elements in perception was constructed. The online evaluation of the static element uncertainty was utilized to infer the dynamic element uncertainty, and then a model for recognizing the driving scenario and weather conditions was constructed to identify the triggering factors of uncertainty in real-time perception during autonomous driving operations, which can further optimize the online assessment model for perception uncertainty. The verification results on the nuScenes dataset show that our uncertainty assessment method based on a high-definition map effectively evaluates the real-time perception results’ performance. Full article

(This article belongs to the Special Issue Sustainable Intelligent and Connected Transportation)

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18 pages, 13992 KiB

Open AccessArticle

Mamdani vs. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models

by Mădălin-Dorin Pop, Dan Pescaru and Mihai V. Micea

Sensors 2023, 23(21), 8791; https://doi.org/10.3390/s23218791 - 28 Oct 2023

Cited by 3 | Viewed by 2352

Abstract

The transition to intelligent transportation systems (ITSs) is necessary to improve traffic flow in urban areas and reduce traffic congestion. Traffic modeling simplifies the understanding of the traffic paradigm and helps researchers to estimate traffic behavior and identify appropriate solutions for traffic control. One of the most used traffic models is the car-following model, which aims to control the movement of a vehicle based on the behavior of the vehicle ahead while ensuring collision avoidance. Differences between the simulated and observed model are present because the modeling process is affected by uncertainties. Furthermore, the measurement of traffic parameters also introduces uncertainties through measurement errors. To ensure that a simulation model fully replicates the observed model, it is necessary to have a calibration process that applies the appropriate compensation values to the simulation model parameters to reduce the differences compared to the observed model parameters. Fuzzy inference techniques proved their ability to solve uncertainties in continuous-time models. This article aims to provide a comparative analysis of the application of Mamdani and Takagi–Sugeno fuzzy inference systems (FISs) in the calibration of a continuous-time car-following model by proposing a methodology that allows for parallel data processing and the determination of the simulated model output resulting from the application of both fuzzy techniques. Evaluation of their impact on the follower vehicle considers the running distance and the dynamic safety distance based on the observed behavior of the leader vehicle. In this way, the identification of the appropriate compensation values to be applied to the input of the simulated model has a great impact on the development of autonomous driving solutions, where the real-time processing of sensor data has a crucial impact on establishing the car-following strategy while ensuring collision avoidance. This research performs a simulation experiment in Simulink (MATLAB R2023a, Natick, MA, USA: The MathWorks Inc.) and considers traffic data collected by inductive loops as parameters of the observed model. To emphasize the role of Mamdani and Takagi–Sugeno FISs, a noise injection is applied to the model parameters with the help of a band-limited white-noise Simulink block to simulate sensor measurement errors and errors introduced by the simulation process. A discussion based on performance evaluation follows the simulation experiment, and even though both techniques can be successfully applied in the calibration of the car-following models, the Takagi–Sugeno FIS provides more accurate compensation values, which leads to a closer behavior to the observed model. Full article

(This article belongs to the Special Issue Sustainable Intelligent and Connected Transportation)

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23 pages, 9979 KiB

Open AccessArticle

Vehicle Detection and Tracking with Roadside LiDAR Using Improved ResNet18 and the Hungarian Algorithm

by Ciyun Lin, Ganghao Sun, Dayong Wu and Chen Xie

Sensors 2023, 23(19), 8143; https://doi.org/10.3390/s23198143 - 28 Sep 2023

Cited by 1 | Viewed by 2846

Abstract

By the end of the 2020s, full autonomy in autonomous driving may become commercially viable in certain regions. However, achieving Level 5 autonomy requires crucial collaborations between vehicles and infrastructure, necessitating high-speed data processing and low-latency capabilities. This paper introduces a vehicle tracking algorithm based on roadside LiDAR (light detection and ranging) infrastructure to reduce the latency to 100 ms without compromising the detection accuracy. We first develop a vehicle detection architecture based on ResNet18 that can more effectively detect vehicles at a full frame rate by improving the BEV mapping and the loss function of the optimizer. Then, we propose a new three-stage vehicle tracking algorithm. This algorithm enhances the Hungarian algorithm to better match objects detected in consecutive frames, while time–space logicality and trajectory similarity are proposed to address the short-term occlusion problem. Finally, the system is tested on static scenes in the KITTI dataset and the MATLAB/Simulink simulation dataset. The results show that the proposed framework outperforms other methods, with F1-scores of 96.97% and 98.58% for vehicle detection for the KITTI and MATLAB/Simulink datasets, respectively. For vehicle tracking, the MOTA are 88.12% and 90.56%, and the ID-F1 are 95.16% and 96.43%, which are better optimized than the traditional Hungarian algorithm. In particular, it has a significant improvement in calculation speed, which is important for real-time transportation applications. Full article

(This article belongs to the Special Issue Sustainable Intelligent and Connected Transportation)

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Review

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20 pages, 1274 KiB

Open AccessReview

Driving Sustainability: Carbon Footprint, 3D Printing, and Legislation concerning Electric and Autonomous Vehicles

by Mihailo Jovanović, Tomás de J. Mateo Sanguino, Milanko Damjanović, Milena Đukanović and Nikolas Thomopoulos

Sensors 2023, 23(22), 9104; https://doi.org/10.3390/s23229104 - 10 Nov 2023

Cited by 3 | Viewed by 1836

Abstract

In recent years, there has been a remarkable development in the technology and legislation related to electric and autonomous vehicles (i.e., EVs/AVs). This technological advancement requires the deployment of the most up-to-date supporting infrastructure to achieve safe operation. Further infrastructure is needed for Level 5 vehicles, namely the introduction of super-fast wireless 5G technology. To achieve harmony between the rapid technological advancement of EVs/AVs and environmental preservation, enacting legislation related to their sustainable use is vital. Thus, this manuscript provides a review of the technological development of EVs/AVs, with a special focus on carbon footprints and the implementation of additive manufacturing using recycled materials. While EVs have a 12.13% increased carbon footprint compared to conventional vehicles, AVs with basic and advanced intelligence features have an increased carbon footprint of 41.43% and 99.65%, respectively. This article emphasizes that the integration of 3D-printed components has the potential to offset this impact with a substantial 60% reduction. As a result, custom-made solutions involving 3D printing are explored, leading to greater speed, customization, and cost-effectiveness for EVs/AVs. This article also lists the advantages and disadvantages of the existing legislation in Spain, the United Kingdom, and the western Balkans, demonstrating various approaches to promoting electric mobility and the development of autonomous vehicles. In Spain, initiatives like the MOVES program incentivize EV adoption, while the UK focuses on expanding the EV market and addressing concerns about EVs’ quiet operation. In the western Balkans, the adoption of legislation lags behind, with limited incentives and infrastructure for EVs. To boost sales, legal mechanisms are necessary to reduce costs and improve accessibility, in addition to offering subsidies for the purchase of EVs. To this end, an analysis of the incentive measures proposed for the development and use of renewable power sources for the supply of energy for EVs/AVs is presented. Full article

(This article belongs to the Special Issue Sustainable Intelligent and Connected Transportation)

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