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Vehicular Ad-Hoc Networks (VANETs) play a crucial role in the development of Intelligent Transport Systems (ITS), leveraging Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X) communication models. These networks, however, face challenges such as node administration, security, and efficient routing, particularly due to the unpredictable nature of vehicle mobility. Traditional routing protocols like Adhoc On-Demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination Sequence Distance Vector (DSDV) are often inadequate for VANETs. Addressing these challenges, we propose FLQL-VANET, a hybrid mechanism that integrates fuzzy logic and Q-learning for enhanced routing. The clustering mechanism in FLQL-VANET selects cluster heads based on parameters such as connectivity, vehicle direction, distance, velocity, path loss, signal quality, and time to leave. Q-learning optimizes the routing path through a reward mechanism, ensuring efficient data transmission. The comparative analysis demonstrates that FLQL-VANET outperforms existing methods such as RMRPTS, ICA-RBF, and KMRP. Our approach achieves an average packet delivery rate of 96.7%, an end-to-end delay of 0.61 seconds, and an average throughput of 868 kbps. These results indicate significant improvements in routing efficiency and reliability, highlighting the potential of FLQL-VANET to enhance VANET performance in real-world scenarios. [ABSTRACT FROM AUTHOR]

Autonomous driving development requires rigorous testing in real-world scenarios, including adverse weather, unpredictable events, object variations, and sensor limitations. However, these challenging "corner cases" are elusive in conventional datasets due to their unpredictability, high costs, and inherent risks. Recognizing the critical role of ground truth data in autonomous driving, the demand for synthetic data becomes evident. Contemporary machine learning-based algorithms essential to autonomous vehicles heavily depend on labeled data for training and validation. Simulation of scenarios not only mitigates the scarcity of real-world data but also facilitates controlled experimentation in situations that are challenging to replicate physically. The challenge extends beyond data scarcity, encompassing the impediment posed by the inability to systematically control and manipulate specific scenarios, hindering progress. To overcome these challenges, we present CornerSim, a dynamic virtualization framework simplifying the creation and modification of diverse driving scenarios. Leveraging simulation, CornerSim generates synthetic environments for comprehensive testing, providing essential outputs like raw sensor data (cameras, LiDAR, etc.) and labeled data (object detection bounding boxes, classes, semantic segmentation). The unpredictable nature of real-world corner cases complicates obtaining a sufficiently large and diverse annotated dataset. CornerSim addresses this challenge by not only generating synthetic data but also supplying necessary ground truth for training and evaluating machine learning models. This paper emphasizes the introduction of CornerSim and its ability to challenges related to testing autonomous vehicles in realistic scenarios. It focuses on the framework's capabilities, design principles, and integration, with the goal of enhancing thorough testing and validation of autonomous driving systems in a simulated environment, improving their robustness and safety. Our approach involves running simulations to generate datasets, which are statistically studied and compared with real data. Furthermore, we apply state-of-the-art detection algorithms to assess if data generated by CornerSim is suitable for both training and validation stages. [ABSTRACT FROM AUTHOR]

In the realm of modern urban mobility, automatic incident detection is a critical element of intelligent transportation systems (ITS), since the ability to promptly identify unexpected events allows for quick implementation of preventive measures and efficient response to the situations as they arise. With the growing availability of traffic data, Machine Learning (ML) has become a vital tool for enhancing traditional incident detection methods. Automated machine-learning (AutoML) techniques present a promising solution by streamlining the machine-learning process; however the application of AutoML for incident detection has not been widely explored in scientific research In this paper, we propose and apply an AutoML-based methodology for traffic incident detection and compare it with state-ofthe-art ML approaches. Our approach integrates data preprocessing with AutoML, and uses Tree-based Pipeline Optimization Tool (TPOT) to refine the process from raw data to prediction. We have tested the efficiency of our approach in two major European cities, Athens and Antwerp. Finally, we present the limitations of our work and outline recommendations for application of AutoML in the incident detection task and potentially in other domains. [ABSTRACT FROM AUTHOR]

On 11 March 2020, the World Health Organisation (WHO) classified the COVID-19 outbreak as a global pandemic and, in consequence, many countries took steps to prevent the importation and subsequent local transmission of the SARS-CoV-2 virus, resulting in restrictions on economic activity, transport, travel, and daily mobility. Although the COVID-19 pandemic and its impact on daily mobility have been widely addressed in the literature, there is a limited number of studies that indicate to what extent these changes have become permanent. The purpose of this study was to determine the nature and scale of the impact of the COVID-19 pandemic on the local transport system of a large city in Poland (Łódź) and, above all, to identify the permanence of this impact. To accomplish these objectives, a questionnaire survey was conducted using the computer-assisted telephone interviewing (CATI) technique on a sample of 500 residents, which included questions on daily mobility in the period before (autumn 2019) and after (autumn 2022) the pandemic. In addition, the results of the questionnaire survey were juxtaposed with data from intelligent transport systems (ITS) (data from 20 induction loops, and data on the number of tickets validated in public transport vehicles). Not only did the pandemic change the frequency of spatial motivations, but it also affected trip durations and preferred modes of transport. The most unfavourable changes concern the modal division and the increase in the use of private transport at the expense of public transport. Understanding the durability of the impact of the pandemic on changes in the spatial mobility of the population may help to develop transport policies and increase the resilience of transport systems to possible future pandemics. [ABSTRACT FROM AUTHOR]

As Intelligent Transport Systems (ITS) continue to evolve, the quest for improving road safety and transportation efficiency has gained renewed emphasis. One of the pivotal aspects in this endeavor is the detection and analysis of driver behavior. Recognizing signs of fatigue, distraction, or inattentiveness is critical in enhancing road safety and optimizing traffic flow. In this paper, we present a pioneering approach to driver behavior detection within the realm of ITS using deep learning models in the Cyber-Physical Systems (CPS) framework. Our research focuses on the discernment of critical behaviors such as eye closure, open-eye state, yawning, and non-yawning instances. With an unwavering commitment to road safety and transportation efficiency, we’ve harnessed the power of deep learning to design, develop, and train an exceptionally accurate model. Through rigorous evaluation, we achieved an impressive 94% accuracy. Our findings unveil the potential of CPS-based solutions for real-time driver behavior monitoring, providing a foundation for safer roadways and more streamlined traffic management. The proposed deep learning model offers robust and accurate predictions, enabling timely responses to various driving conditions. This research significantly advances the field of driver behavior analysis within the context of intelligent transportation systems, with broad implications for road safety and traffic management.

Blockchain technology has been widely used in finance, transportation, education, medical treatment, network security, management science, and other industries due to its decentralization, high reliability, and traceability characteristics. Unlike other studies on Urban Intelligent Transportation Systems (UITS) in the past, we present a model framework of the Urban Intelligent Transportation Systems (UITS) applied by blockchain in developing countries. After a detailed elaboration of the situation of three representative Urban Intelligent Transportation Systems (UITS) in China, blockchain technology has been applied to build a new architecture model of the big data platform for urban intelligent transportation, as well as the design concept and conceptual model for the new generation of the Urban Intelligent Transportation Systems (UITS) in developing countries. Finally, this paper elaborates on the critical direction of future development, areas of concern, and open research challenges, which researchers and urban intelligent transportation designers could explore to further advance in this field.

Traffic congestion has become one of the most pressing social problems in today's society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. As an alternative, we propose a swarm intelligence-based methodology that creates routes with a similar traffic volume using the traffic information from intersections already equipped with sensors and interpolates this information in the intersections without sensors in real time. Our simulation results show that the proposed methodology can effectively create similar traffic routes for main traffic flows with high traffic volumes. The results also show that it has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. Moreover, the interpolation results are highly accurate at a road link where traffic flows confluence. We also developed an interpolation algorithm that is adaptable to traffic patterns with confluence traffic flows. Experiments were conducted with a simulation of merging traffic flows and the proposed method showed good results. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Networks (VANETs) are a challenging yet active research area. It offers a wide range of applications, including Intelligent Transport System (ITS), effective road traffic monitoring, efficient traffic flow and road safety applications. During real-time data gathering for emergency scenarios, the fixed silent segments cause a problem for smooth communication. Moreover, the critical ITS operations may be delayed due to this problem. This paper proposes a Real-Time Traffic-Aware Data Gathering Protocol (TDG) where the dynamic segmentation switching is adopted to handle the communication limitations. TDG is lightweight and dynamically designed for collecting and forwarding data packets based on current and rapid evolving traffic conditions. The primary objective is to reduce network and data communication overhead to incorporate real-time data collection time constraints. TDG implements a data aggregation scheme for data analysis to fetch information based on location, speed, vehicle id and neighbour count. Moreover, a data extraction scheme is implemented to increase data retrieval and data utilization effectiveness in an intelligent way at the base station. Extensive simulation and evaluation results validate that our proposed solution outperforms existing data gathering protocols in effectiveness, efficiency, delay, communication overhead and data transmission rate.

This paper identifies potential barrier factors affecting effectiveness and development (ED) of ITS projects as well as criteria for measuring ED of ITS projects in Ho Chi Minh City, Vietnam. The study discovers the barrier constructs, and analyzes data using the Partial Least Squares Structural Equation Modeling method (PLS-SEM). The results provides a general and comprehensive overview of the main issues of ITS, and identifies 28 barrier factors with five main constructs affecting ED of ITS projects, namely the lack of undivided attention from the government (AG), financial constraints for ITS (FC), inadequate transport infrastructure (ITI), the over-development of urbanization (ODU), and the readiness and integration for ITS (RI). This paper fill the knowledge gap by discovering the causal relationships between barrier constructs and ED of ITS projects in Vietnam. Also it proposes several solutions for these issues, which are also a useful measurement tool for government agencies, planners, and traffic system designers to help them self-assess and make action plans now or in the near future.

Intelligent transport systems (ITS) are undoubtedly an opportunity for the sustainable development of smart cities today. ITS is based on advanced transport technologies that help minimise the emission of harmful substances to the environment. Smart mobility and ITS are related to the use of ICT. The implementation of technologically advanced ITS is associated with several benefits, barriers and difficulties. However, transport, ITS and smart mobility (as a component of a smart city) are indicated as the most desirable option for sustainable urban transport systems. The article aims to identify barriers related to the implementation of ITS in cities from the point of view of people responsible for the organisation of urban transport representing the local government of selected voivodship cities in Poland. The goal formulated in this way allowed to identify the following research question: what are the problems and barriers of implementing ITS in the city from the local government’s perspective? To achieve the paper’s aim, the author based their analysis on a qualitative technique of collecting empirical data. Ten individual in-depth interviews were conducted with representatives of local governments (vice-mayors and members of urban transport organisers) in voivodship cities, which represented six Polish macro-regions. Research results and findings indicate the main categories and subcategories of barriers related to the ITS implementation. The identified barriers are grouped into the following categories: economic, social, organisational, technological and legal. The contribution is twofold: first, in the presentation of the theoretical and practical barriers to ITS in juxtaposition; and second, in identifying the intelligent transportation impact, which affects the provision of being a smarter city. The findings can positively influence as important factors for local governments to focus on intelligent transport.

The security and reliability of communication systems are crucial for Intelligent Transport Systems (ITS), ensuring user data privacy and the transportation network's stable operation. To address challenges where certain Vehicle User Equipment (VUE) cannot establish stable message exchange with the Base Station (BS), we employ Internet of Things (IoT) nodes to assist in communication setup. Specifically, we propose a weighted fractional Fourier transform (WFRFT)-based two-way relay physical layer security (PLS) communication scheme in the signal domain for IoT-aided ITS. The BS and VUE encode confidential messages into WFRFT signals with private transform orders and perfectly decode the received signal in the specific signal domain. However, in the decoding results at the eavesdropper, not only is the energy of information-bearing signals reduced, but the lost energy also transforms into inter-component interference, further degrading the quality of its received signal. The security gains stem from the mismatched signal domain of the eavesdropper and do not require additional transmit power. Moreover, the security performance is detailed, and the secrecy rate in a time-varying channel is derived. Finally, numerical results validate the theoretical analysis and demonstrate the performance of the proposed scheme. • Propose a two-way relay PLS communication system for ITS. • Propose a WFRFT-based PLS scheme in the signal domain. • The security performance is analyzed theoretically and substantiated. [ABSTRACT FROM AUTHOR]

Purpose: This study evaluates the acceptance of GPS/GPRS-based telematics technology in freight road transport companies registered in Poland. Design/methodology/approach: The evaluation is based on a survey of 500 representative road transport companies carried out in 2020. The Technology Acceptance Model was estimated, and its results were checked for robustness. The scope of the information collected in telematics systems is defined in terms of its perceived utility and perceived ease of use at the operational management level. The latent factors affecting technology use are defined and implemented. Findings: Most respondents (80%) claimed that telematics systems had a considerable influence on the effectiveness and efficiency of the whole company's operation. It contributed to a higher number of orders executed per time unit, more effective use of the driver's working time, and increased the entrepreneurs' trust in the company. The companies employing more workers recognize the higher usefulness of telematics systems and are motivated to use the technology more widely than smaller enterprises. TAMs estimated separately for small and medium-sized enterprises did not significantly differ among the parameter estimates. Research limitations/implications: The Technology Acceptance Model is a useful analytical tool for evaluating telematics technology acceptance by the road transport sector. The study is based on a random sample of enterprises observed once in 2020. It is recommended to monitor them in two or three waves to compare the dynamics of the telematics usage process. It is planned to continue the study in that direction. Practical implications: The outcomes are valuable in practice twofold. Firstly, the extension of telematics systems use is interesting for final users, i.e., road transport companies that will find scope for their application. Secondly, the results are helpful for system providers who get knowledge on telematics perception from enterprise management. Originality/value: Although widely applied to other IT systems, the TAM model has not been used to evaluate the use of telematics in road transport companies. The paper justifies TAM's categories at the operational management level in road transport enterprises, contributing to understanding managers' behavioral aspects of decision-making. [ABSTRACT FROM AUTHOR]

The development and introduction of intelligent transport systems (ITS) services are the main steps that will stimulate the application of new information and communication technologies in the transport system. The conditions of openness, interoperability, and flexibility of the system strengthen the active role of the industry in the development, construction, introduction, and impact of ITS. The implementation of these systems with it brings numerous benefits: reduction of traffic congestion and travel time, reduced fuel consumption and environmental pollution, and increased safety of pedestrians and cyclists. This paper aims to present the methodology for designing the ITS architecture in the Republic of North Macedonia. The projected national ITS architecture and action plan set the framework for planning, analyzing, defining, deploying, and integrating ITS, while providing an understanding of their business, organizational and technical implications, integrating the three main elements: required functions, system participation in logical or functional entities such as subsystems and the flow of information and data that link these functions. [ABSTRACT FROM AUTHOR]

In ITS environment, the driver-assistant services usually demand delivering video content to vehicular users, which is very useful in the intervention of emergency services, and reminds the drivers the traffic jam in ahead road. Nowadays for online video delivery in connected vehicle networks, HTTP adaptive streaming (HAS) is one of the most promising technologies, where a video is encoded into multiple bitrates and then split into small segments. It is crux for ITS service providers how to improve users' QoE through an adaptive video segments request strategy. From the perspective of adaptive matching among segment bitrate, network bandwidth, and buffer occupancy, we propose a bandwidth-aware video segment request strategy to optimize user's QoE (for short Bw-QoE), which consists of two parts, i.e., available bandwidth estimation, and segment's bitrate selection. First, based on the severity of bandwidth fluctuation, Bw-QoE estimates the available bandwidth with different historical weighted average methods. Then constrained by the estimated available bandwidth and buffer occupancy, the adaptive bitrate selection is formulated as an optimization model to maximize user's QoE of a video streaming session. It is fairly attractive for Bw-QoE to prevent buffer overflow, underflow and unuseful download, and to reduce bitrate switching. Compared with the classic strategy (e.g., LIU's strategy), the simulation results show that the proposed strategy Bw-QoE has higher average video bitrate (about 8%-10%), less interruption (about 100%), less segment bitrate switching (about 15%-17%) and higher buffer occupancy (about 13%-51%), which demonstrate that our strategy effectively improves QoE.

Over the last ten years, Vehicular Ad Hoc Networks (VANETs) have received significant attention from the academic and industrial communities alike. VANETs are a particular type of mobile ad hoc network originally designed for the purpose of facilitating the creation of spontaneous wireless networks between different vehicles, but since their inception the scope of VANETs has been extended to other types of road users such as cyclists and pedestrians. Due to the volatility of the wireless medium, VANETs face several challenges, especially when applications with a diverse set of requirements must be supported. Among the various techniques used to address such challenges, one of the most recent is Software-Defined Networking (SDN), which, by clearly separating the data plane from the control plane, allows the implementation of traditional network control and management tasks on top of a logically centralized controller. In this work, we perform a systematic review of SDN techniques tailored to the VANET domain. More specifically, we first review the literature on VANETs and SDN from an architectural and communications requirement perspective, then we report on the most recent standardization efforts, and finally, we highlight the open research areas and the most important challenges in this domain.

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This paper investigates the vehicle classification (VC) based on vehicular ad-hoc networks (VANETs). Using VANETs, one can extract the physical and mobility characteristics of the vehicles globally and in a real-time manner. In this paper, we propose an in-depth novel safety-driven VC method for heterogeneous connected vehicles. In this innovative approach, road vehicles are classified into a broad range of classes according to their distinctive behaviors and safety measures. The proposed method can play a vital role in reducing collisions and can be used as a safety standard reference in VANETs-based VC systems. Furthermore, advance driver assistance systems (ADAS) can integrate this method and extend road safety by notifying vehicles of dangerous situations on the road using V2X communication. [ABSTRACT FROM AUTHOR]

In a vehicular environment, by becoming connected, vehicles are subject to more threats in comparison to traditional information systems, with the difference that, as a cyber-physical system, anomalies and intrusions could have repercussions in the physical world. In this work, we have developed an ontological anomaly-detection approach (OADA). The anomalies studied in this work mainly concern: network scans, DNS tunnel attacks, and telemetry data anomalies. Our contribution relates to a study of the attributes of interest for the algorithm used during the detection phase, namely the hierarchical temporal memory algorithm (HTM). The packets exchanged by the vehicle are grouped in instant description windows. These windows are then analyzed to extract a set of attributes. These are linked to the properties of network traffic, such as flow or latency. They are subject to the process of detecting anomalies and intrusions, carried out thanks to the algorithm with HTM. For each entry, the algorithm produces a score that allows us to decide if a window is abnormal and to lift an alert if that is the case. We evaluated our system using a communications and anomalies emulation tool. We use the corpus of data produced thanks to Autobot. We seek to determine from among the best scores of Matthews correlation coefficient (MCC) and Detection efficiency score (DES) which were the parameters for which HTM detects all anomalies with the greatest possible coverage. The obtained results prove that HTM can detect all anomalies for each window duration. [ABSTRACT FROM AUTHOR]

The urban mobility is affected by global trends resulting in a growing passenger and freight transport demand. In order to improve the understanding of urban mobility in general, to evaluate mobility services and to quantify the overall transport system performance, it is necessary to assess urban mobility. Urban mobility assessment requires the application of methodology integrating different metrics and explicitly applying a multi-dimensional approach. Since scientific community does not define urban mobility in an unambiguous way, part of this paper is devoted to the analysis of the definition of urban mobility. This step enables better understanding of urban mobility in general, as well as understanding of the urban mobility assessment process. Usually, a three-layered approach that includes urban mobility data, indicators and indices is used for the assessment. Therefore, the aim of this paper was to perform extensive research in order to synthesize, define and organize the elements of those layers. The existing urban mobility indicators and indices have been developed for specific urban areas, taking into account local specifications, and they are not applicable in other cities. Also, the choice of urban mobility indicators is mainly related to the existence of data sources, which limits the objective and comparable assessment of the mobility of cities where such data do not exist.

Traffic flow exhibits different magnitudes of temporal patterns, such as short-term (daily and weekly) and long-term (monthly and yearly). Existing research into road traffic flow prediction has focused on short-term patterns; little research has been done to determine the effect of different long-term patterns on road traffic flow prediction. Providing more temporal contextual information through the use of different temporal data segments could improve prediction results. In this paper, we have investigated different magnitudes of temporal patterns, such as short-term and long-term, through the use of different temporal data segments to understand how contextual temporal data can improve prediction. Furthermore, to learn temporal patterns dynamically, we have proposed a novel online dynamic temporal context neural network framework. The framework uses different temporal data segments as input features, and during online learning, the updating scheme dynamically determines how useful a temporal data segment (short and long-term temporal patterns) is for prediction, and weights it accordingly for use in the regression model. Therefore, the framework can include short-term and relevant long-term patterns in the regression model leading to improved prediction results. We have conducted a thorough experimental evaluation with a real dataset containing daily, weekly, monthly and yearly data segments. The experiment results show that both short and long-term temporal patterns improved prediction accuracy. In addition, the proposed online dynamical framework improved predication results by 10.8% when compared with a deep gated recurrent unit model.

This study investigated the possibility of using an intelligent transport system (ITS) and geographic information system (GIS) to increase the efficiency of the campus transportation at King Saud University in Riyadh, Saudi Arabia. The study yielded several findings, prior to the implementation of the GIS-based ITS, the time spent waiting for the university bus amounted to less than 5 min (19% of responses), 5–10 min (48%), 10–20 min (30%), and more than 30 min (3%). Regarding the bus arrival time at home, 62% of the female students did not know the bus arrival time, and 38% knew the bus arrival time. However, after the creation of an intelligent transportation system that sent an alert prior to the arrival of the bus in front of their houses, the time the female students spent waiting for the bus decreased significantly, reaching 14 min, in addition to the 10 min spent for the bus to move between houses. The constant factor before and after the ITS intervention was the total time required to pick up the female students, reaching 24 min, and the new system saved 43 min of daily trip time. [ABSTRACT FROM AUTHOR]

Copyright of Reforma (1694-5158) is the property of Kyrgyz-Turkish Manas University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intelligent transport systems (ITS) are a promising area of studies. One implementation of ITS are advanced driver assistance systems (ADAS), involving the problem of obstacle detection in traffic. This study evaluated the YOLOv4 model as a state-of-the-art CNN-based one-stage detector to recognize traffic obstacles. A new dataset is proposed containing traffic obstacles on Indonesian roads for ADAS to detect traffic obstacles that are unique to Indonesia, such as pedicabs, street vendors, and bus shelters, and are not included in existing datasets. This study established a traffic obstacle dataset containing eleven object classes: cars, buses, trucks, bicycles, motorcycles, pedestrians, pedicabs, trees, bus shelters, traffic signs, and street vendors, with 26,016 labeled instances in 7,789 images. A performance analysis of traffic obstacle detection on Indonesian roads using the dataset created in this study was conducted using the YOLOv4 method. [ABSTRACT FROM AUTHOR]

Vehicular networks use Decentralized Congestion Control (DCC) mechanisms to operate effectively, but this mechanism may introduce queuing delays. Freshness of Cooperative Awareness Messages (CAMs) is critical for their usefulness. In this letter we explore how the presence of other types of traffic additional to CAMs, even with lower priorities, has an impact on the freshness of CAM messages due to DCC queuing. Finally, we propose Generate-on-Time (GoT), which is a simple mechanism that reduces DCC queuing delays for CAM messages without introducing any downside in other performance metrics. [ABSTRACT FROM AUTHOR]

A Vehicular Ad-Hoc Network (VANET) is a class of a Mobile Ad-Hoc Network (MANET). VANETs help in the implementation of various driving safety applications. Vehicle to Vehicle Communication is wireless transmission of data that provides services for important safety improvements. The adoption of video transmission in VANETs communication has become essential due to the comprehensiveness and applicability of video data for on-road advertisement and infotainment. Video communication in VANETs helps in capturing and monitoring the environment, surveillance, traffic accidents and disaster based smart city applications. In this paper, we propose a real-time system where two vehicles communicate via video streaming. It can be used in an isolated place without the internet. We further propose a system where the first vehicle which detects an accident sends a video stream of the accident scene to the second vehicle so that the second vehicle can take an alternate path to reduce the traffic at the place of accident. [ABSTRACT FROM AUTHOR]

Signal identification is of great interest for various applications such as spectrum sharing and interference management. A typical signal identification system can be divided into two steps. A feature vector is first extracted from the received signal, then a decision is made by a classification algorithm according to its observed values. Some existing techniques show good performance but they are either sensitive to noise level or have high computational complexity. In this paper, a machine learning algorithm is proposed for the identification of vehicular communication signals. The feature vector is made up of Instantaneous Frequency (IF) resulting from time–frequency (TF) analysis. Its dimension is then reduced using the Singular Value Decomposition (SVD) technique, before being fed into a Random Forest classifier. Simulation results show the relevance and the low complexity of IF features compared to existing cyclostationarity-based ones. Furthermore, we found that the same accuracy can be maintained regardless of the noise level. The proposed framework thus provides a more accurate, robust and less complex V2X signal identification system.

The opportunistic exchange of information between vehicles can significantly contribute to reducing the occurrence of accidents and mitigating their damages. However, in urban environments, especially at intersection scenarios, obstacles such as buildings and walls block the line of sight between the transmitter and receiver, reducing the vehicular communication range and thus harming the performance of road safety applications. Furthermore, the sizes of the surrounding vehicles and weather conditions may affect the communication. This makes communications in urban V2V communication scenarios extremely difficult. Since the late notification of vehicles or incidents can lead to the loss of human lives, this paper focuses on improving urban vehicle-to-vehicle (V2V) communications at intersections by using a transmission scheme able of adapting to the surrounding environment. Therefore, we proposed a neuroevolution of augmenting topologies-based adaptive beamforming scheme to control the radiation pattern of an antenna array and thus mitigate the effects generated by shadowing in urban V2V communication at intersection scenarios. This work considered the IEEE 802.11p standard for the physical layer of the vehicular communication link. The results show that our proposal outperformed the isotropic antenna in terms of the communication range and response time, as well as other traditional machine learning approaches, such as genetic algorithms and mutation strategy-based particle swarm optimization.