Vehicle-to-Pedestrian Communication for Vulnerable Road Users: Survey, Design Considerations, and Challenges

Task Offloading Decision-Making Algorithm for Vehicular Edge Computing: A Deep-Reinforcement-Learning-Based Approach

Efficient task offloading decision is a crucial technology in vehicular edge computing, which aims to fulfill the computational performance demands of complex vehicular tasks with respect to delay and energy consumption while minimizing network resource competition and consumption. Conventional distributed task offloading decisions rely solely on the local state of the vehicle, failing to optimize the utilization of the server's resources to its fullest potential. In addition, the mobility aspect of vehicles is often neglected in these decisions. In this paper, a cloud-edge-vehicle three-tier vehicular edge computing (VEC) system is proposed, where vehicles partially offload their computing tasks to edge or cloud servers while keeping the remaining tasks local to the vehicle terminals. Under the restrictions of vehicle mobility and discrete variables, task scheduling and task offloading proportion are jointly optimized with the objective of minimizing the total system cost. Considering the non-convexity, high-dimensional complex state and continuous action space requirements of the optimization problem, we propose a task offloading decision-making algorithm based on deep deterministic policy gradient (TODM_DDPG). TODM_DDPG algorithm adopts the actor-critic framework in which the actor network outputs floating point numbers to represent deterministic policy, while the critic network evaluates the action output by the actor network, and adjusts the network evaluation policy according to the rewards with the environment to maximize the long-term reward. To explore the algorithm performance, this conduct parameter setting experiments to correct the algorithm core hyper-parameters and select the optimal combination of parameters. In addition, in order to verify algorithm performance, we also carry out a series of comparative experiments with baseline algorithms. The results demonstrate that in terms of reducing system costs, the proposed algorithm outperforms the compared baseline algorithm, such as the deep Q network (DQN) and the actor-critic (AC), and the performance is improved by about 13% on average.

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

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.

Assessment of Electromagnetic Field Exposure on European Roads: A Comprehensive In Situ Measurement Campaign

The rapid evolution of wireless communication technologies (such as fifth-generation (5G) cellular networks) in the last years has allowed connecting different objects (from wearable electronics to vehicles) and people through communication networks, and at the same time, has led to widespread deployment of base stations. Along with this growth, questions about the potential adverse effects on human health due to electromagnetic fields (EMFs) from base station antennas have also been raised. In this paper, we focus on the assessment of EMFs in automobiles during short (between cities) and long (between countries) trips on several European roads. Comprehensive measurement campaigns were carried out in several European countries: Austria, Bulgaria, Croatia, Hungary, Italy, Slovenia, and the Republic of Serbia. The results show that the median total electric field is 0.23-0.24 V/m in Bulgaria, Croatia, Hungary, Italy, and the Republic of Serbia. In Austria and Slovenia, the median is 0.28-0.31 V/m. Austria demonstrated the highest value for the total electric field, at 17.4 V/m.

Electronic textiles: New age of wearable technology for healthcare and fitness solutions

Sedentary lifestyles and evolving work environments have created challenges for global health and cause huge burdens on healthcare and fitness systems. Physical immobility and functional losses due to aging are two main reasons for noncommunicable disease mortality. Smart electronic textiles (e-textiles) have attracted considerable attention because of their potential uses in health monitoring, rehabilitation, and training assessment applications. Interactive textiles integrated with electronic devices and algorithms can be used to gather, process, and digitize data on human body motion in real time for purposes such as electrotherapy, improving blood circulation, and promoting wound healing. This review summarizes research advances on e-textiles designed for wearable healthcare and fitness systems. The significance of e-textiles, key applications, and future demand expectations are addressed in this review. Various health conditions and fitness problems and possible solutions involving the use of multifunctional interactive garments are discussed. A brief discussion of essential materials and basic procedures used to fabricate wearable e-textiles are included. Finally, the current challenges, possible solutions, opportunities, and future perspectives in the area of smart textiles are discussed.

An approach to pedestrian walking behaviour classification in wireless communication and network failure contexts

Despite the wide range of research on pedestrian safety, previous studies have failed to analyse the real-time data of pedestrian walking misbehaviour on the basis of either pedestrian behaviour distraction or movements during specific activities to realise pedestrian safety for positive (normal) or aggressive pedestrians. Practically, pedestrian walking behaviour should be recognised, and aggressive pedestrians should be differentiated from normal pedestrians. This type of pedestrian behaviour recognition can be converted into a classification problem, which is the main challenge for pedestrian safety systems. In addressing the classification challenge, three issues should be considered: identification of factors, collection of data and exchange of data in the contexts of wireless communication and network failure. Thus, this work proposes a novel approach to pedestrian walking behaviour classification in the aforementioned contexts. Three useful phases are proposed for the methodology of this study. In the first phase involving factor identification, several factors of the irregular walking behaviour of mobile phone users are established by constructing a questionnaire that can determine users’ options (attitudes/opinions) about mobile usage whilst walking on the street. In the second phase involving data collection, four different testing scenarios are developed to acquire the real-time data of pedestrian walking behaviour by using gyroscope sensors. In the third phase involving data exchange, the proposed approach is presented on the basis of two modules. The first module for pedestrian behaviour classification uses random forest and decision tree classifiers part of machine learning techniques via wireless communication when a server becomes available. The developed module is then trained and evaluated using five category sets to obtain the best classification of pedestrian walking behaviour. The second module is based on four standard vectors for classifying pedestrian walking behaviour when a server is unavailable. Fault-tolerant pedestrian walking behaviour is identified and is initiated when failures occur in a network. Two sets of real-time data are presented in this work. The first dataset is related to the questionnaire data from 262 sampled respondents, and the second dataset comprises data on 263 sampled participants with pedestrian walking signals. Experimental results confirm the efficacy of the proposed approach relative to previous ones.

Open-Source MQTT-Based End-to-End IoT System for Smart City Scenarios

Many innovative services are emerging based on the Internet of Things (IoT) technology, aiming at fostering better sustainability of our cities. New solutions integrating Information and Communications Technologies (ICTs) with sustainable transport media are encouraged by several public administrations in the so-called Smart City scenario, where heterogeneous users in city roads call for safer mobility. Among several possible applications, recently, there has been a lot of attention on the so-called Vulnerable Road Users (VRUs), such as pedestrians or bikers. They can be equipped with wearable sensors that are able to communicate their data through a chain of devices towards the cloud for agile and effective control of their mobility. This work describes a complete end-to-end IoT system implemented through the integration of different complementary technologies, whose main purpose is to monitor the information related to road users generated by wearable sensors. The system has been implemented using an ESP32 micro-controller connected to the sensors and communicating through a Bluetooth Low Energy (BLE) interface with an Android device, which is assumed to always be carried by any road user. Based on this, we use it as a gateway node, acting as a real-time asynchronous publisher of a Message Queue Telemetry Transport (MQTT) protocol chain. The MQTT broker is configured on a Raspberry PI device and collects sensor data to be sent to a web-based control panel that performs data monitoring and processing. All the architecture modules have been implemented through open-source technologies. The analysis of the BLE packet exchange has been carried out by resorting to the Wireshark packet analyzer. In addition, a feasibility analysis has been carried out by showing the capability of the proposed solution to show the values gathered through the sensors on a remote dashboard. The developed system is publicly available to allow the possible integration of other modules for additional Smart City services or extension to further ICT applications.

A context-aware driver model for determining recommended speed in blind intersection situations

The near-miss events involving vulnerable road users can lead to serious accidents. Safe and careful expert drivers perform a hazard-anticipatory driving and they will naturally seek to reduce the uncertainty by attempting to fit their current driving context into a pre-existing category they have already developed, that is, predicting what can happen. In this study, our target situation consists of a cyclist attempting a road crossing at a blind spot. This study aims at developing a context-aware driver model for determining the recommended driving speed at blind intersections based on the analysis of near-miss-incidence database, which includes the data on driver behavior and road environmental factors just before the near-miss. First, we extracted the drive-recorder data using the management tool provided in the database. Second, risk, which is defined as the time margin for drivers to perform evasive actions to avoid a crash, was quantified for the extracted data using the safety-cushion time. The safety-cushion time can be observed as a result of the driver's adjustment to the vehicle velocity depending on the given road environment. One of the key aspects in developing the context-aware driver model is to categorize the extracted near-miss data into two levels based on the risk quantifications: low- and high-risk events. The low- and high-risk events were regarded as a result of the driver's appropriate adjustment of, and inability or failure to adjust the vehicle velocity depending on the given road environment, respectively. Third, based on a multiple linear regression analysis with low-risk event dataset, we constructed a context-aware driver model to produce the recommended vehicle speed depending on the given road environment. The road environment variables, determined by stepwise regression, were identified as factors that reduced or increased the vehicle velocity at blind intersections, and were incorporated into the model as predictors. Furthermore, we quantitatively visualized drivers setting the baseline for speed adjustment and increasing or decreasing the speed according to the given road environment context. Fourth, the model validation demonstrated a coefficient of determination (R²) of 0.20, and a mean absolute error (MAE) of 6.54 km/h on average in the 5-fold cross-validation. Finally, to investigate the effectiveness of the constructed driver model on safety performance, we used the dataset of high-risk events as test data. Theoretically, the constructed driver model guided the drivers to drive the vehicle at the recommended speed, and thus convert more than half of the high-risk events into low-risk events. These results indicate that the context-aware driver model is feasible to be used to adjust the approaching speed at blind intersections in accordance with the road environment factors.

Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities

In an effort to reach accident-free milestones or drastically reduce/eliminate road fatalities rates and traffic congestion and to create disruptive, transformational mobility systems and services, different parties (e.g., automakers, universities, governments, and road traffic regulators) have collaborated to research, develop, and test connected vehicle (CV) technologies. CVs create new data-rich environments and are considered key enablers for many applications and services that will make our roads safer, less congested, and more eco-friendly. A deeper understanding of the CV technologies will pave the way to avoid setbacks and will help in developing more innovative applications and breakthroughs. In the CV paradigm, vehicles become smarter by communicating with nearby vehicles, connected infrastructure, and the surroundings. This connectivity will be substantial to support different features and systems, such as adaptive routing, real-time navigation, and slow and near real-time infrastructure. Further examples include environmental sensing, advanced driver-assistance systems, automated driving systems, mobility on demand, and mobility as a service. This article provides a comprehensive review on CV technologies including fundamental challenges, state-of-the-art enabling technologies, innovative applications, and potential opportunities that can benefit automakers, customers, and businesses. The current standardization efforts of the forefront enabling technologies, such as Wi-Fi 6 and 5G-cellular technologies are also reviewed. Different challenges in terms of cooperative computation, privacy/security, and over-the-air updates are discussed. Safety and non-safety applications are described and possible future opportunities that CV technology brings to our life are also highlighted.

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.

A Smart City Service Business Model: Focusing on Transportation Services

Most cities have adopted smart city services to solve urban problems. However, an examination of their operations reveals that many of these services have either been discontinued or have failed to advance further since they were not profitable. Therefore, this study reviews and proposes the business models of smart city services at a fundamental level. It defines and classifies the smart city service focusing on transportation and the components. The business model has been constructed for electric vehicles and autonomous shuttle businesses in terms of transportation services. It found that the model was profitable in each business only when various stakeholders were linked for mutual interests. Since various service stakeholders cooperate in smart city service, if one of them is unable to secure profitability, it is difficult to operate the smart city service fully. Therefore, a detailed review of the business model is required before providing a smart city service.

Impact of Safety Message Generation Rules on the Awareness of Vulnerable Road Users

In the face of cooperative intelligent transportation systems (C-ITS) advancements, the inclusion of vulnerable road users (VRU), i.e., pedestrians, cyclists, and motorcyclists, has just recently become a part of the discussion. Including VRU in C-ITS presents new challenges, most notably the trade-off between the increase in VRU safety and the aggravation in channel congestion resulting from VRU-generated messages. However, previous studies mainly focus on network-related metrics without giving much consideration to VRU safety-related metrics. In this context, we evaluated such a trade-off with a study of motion-based message generation rules for VRU transmissions. The rules were analyzed using theoretical and simulation-based evaluations. In addition to studying the message generation rules using channel load metrics, such as channel busy ratio (CBR) and packet delivery ratio (PDR), we introduced a new metric: the VRU Awareness Probability (VAP). VAP uses the exchange of messages from active VRU to measure the probability of VRU detection by nearby vehicles. Results show that fixed message-filtering mechanisms reduce the overall channel load, but they could negatively impact VRU detection. We established the importance of quantifying the VRU awareness and its inclusion in C-ITS analysis because of its direct impact on VRU safety. We also discussed approaches that include VRU context and dynamism to improve the definition of message generation rules.

Evaluating the Impact of Drone Signaling in Crosswalk Scenario

The characteristic pillars of a city are its economy, its mobility, its environment, its inhabitants, its way of life, and its organization. Since 1980, the concept of smart city generally consists of optimizing costs, organization, and the well-being of inhabitants. The idea is to develop means and solutions capable of meeting the needs of the population, while preserving resources and the environment. Owing to their little size, their flexibility, and their low cost, Unmanned Aerial Vehicles (UAV) are today used in a huge number of daily life applications. UAV use cases can be classified into three categories: data covering (like surveillance and event covering), data relaying (like delivery and emergency services), and data dissemination (like cartography and precise agriculture). In addition, the interest to Cooperative Intelligent Transportation Systems (C-ITS) has risen in these recent years, especially in the context of smart cities. In such systems, both drivers and traffic managers share the information and cooperate to coordinate their actions to ensure safety, traffic efficiency, and environment preservation. In this work, we aimed at introducing a UAV in a use case that is likely to happen in C-ITS. A conflict is considered involving a car and a pedestrian. A UAV observes from the top of the scene and will play the role of the situation controller, the information collector, and the assignment of the instructions to the car driver in case of a harmful situation to avoid car-pedestrian collision. To this end, we highlight interactions between the UAV and the car vehicle (U2V communication), as well as between the UAV and infrastructure (U2I communication). Hence, the benefit of using UAV is emphasized to reduce accident gravity rate, braking distance, energy consumption, and occasional visibility reduction.

Evaluation of Push and Pull Communication Models on a VANET with Virtual Traffic Lights

It is expected in a near future that safety applications based on vehicle-to-everything communications will be a common reality in the traffic roads. This technology will contribute to improve the safety of vulnerable road users, for example, with the use of virtual traffic light systems (VTLS) in the intersections. This work implements and evaluates a VTLS conceived to help the pedestrians pass safely the intersections without real traffic lights. The simulated VTLS scenario used two distinct communication paradigms—the pull and push communication models. The pull model was implemented in named data networking (NDN), because NDN uses natively a pull-based communication model, where consumers send requests to pull the contents from the provider. A distinct approach is followed by the push-based model, where consumers subscribe previously the information, and then the producers distribute the available information to those consumers. Comparing the performance of the push and pull models on a VANET with VTLS, it is observed that the push mode presents lower packet loss and generates fewer packets, and consequently occupies less bandwidth, than the pull mode. In fact, for the considered metrics, the VTLS implemented with the pull mode presents no advantage when compared with the push mode.

A New Roadway Eventual Obstacle Detection System Based on Computer Vision

A new roadway eventual obstacle detection system based on computer vision is described and evaluated. This system uses low-cost hardware and open-source software to detect and classify moving elements in roads using infra-red and colour video images as input data. This solution represents an important advancement to prevent road accidents due to eventual obstacles which have considerably increased in the past decades, mainly with wildlife. The experimental evaluation of the system demonstrated that the proposed solution detects and classifies correctly different types of moving obstacles on roads, working robustly under different weather and illumination conditions.

Wi-Fi Direct Performance Evaluation for V2P Communications

The high cost of IEEE 802.11p-compliant devices and the lack of a widely adopted standard motivate the search for alternative methods for vehicular communication. As a consequence, and due to the ubiquity of smartphones, we see an increasing number of proposals based on Wi-Fi Direct, especially related to the integration of pedestrians in VANETs. Nevertheless, given the complexity of real experimentation, it is difficult to assess the ability of Wi-Fi Direct to offer connectivity in the vehicular environment on a large scale, leading to the evaluation through simulation. In order to verify the ability of a simulation model of reproducing the Wi-Fi Direct behavior, we analyze the performance of a safety application based on the communication range, packet delivery rate, and packet inter-reception time. In V2P scenarios, with and without line of sight, with varying vehicle speed, measurements using smartphones are performed and their results compared with those of OMNeT++ simulator. The results indicate, apart from the simulator’s accuracy, that the connection establishment time hinders the use of Wi-Fi Direct as a replacement to 802.11p. As an outcome of this observation, we evaluate a new transmission method for Wi-Fi Direct based on beacon stuffing, which mitigates the long connection establishment issue.

The Use of Meta-Surfaces in Vehicular Networks

Mobility as a service is becoming a new paradigm in the direction of travel planning on the basis of the best service offered by the travelled roads. Hence, the environment in which people move will become smarter and more and more connected to grant services along the whole path. This opens new challenges related not only to the on board connectivity and wireless access technologies, but also on the reliability and efficiency of the surrounding environment. In this context, reconfigurable meta-surfaces play a crucial role, since they can be used to coat buildings, vehicles or any other suitable surfaces and let the environment become an active part of the communication system by opportunistically redirecting (i.e., reflecting, without generating new waves) signals to the target receivers. The objective of this paper is to highlight the limits of current wireless access technologies for vehicular scenarios and to discuss the potential impact of a smart environment made of reconfigurable meta-surfaces on some next generation vehicular use cases, such as cooperative driving and vulnerable road users (VRUs) detection. In addition, a preliminary model is presented to derive, in a simplified way, the performance of an IEEE 802.11p network in terms of collision probability. Even if analytical and based on simplified assumptions, this model has been validated through simulations and allows to compare the performance of the network with and without reconfigurable meta-surfaces.

External Human-Machine Interfaces for Autonomous Vehicle-to-Pedestrian Communication: A Review of Empirical Work

Interaction between drivers and pedestrians is often facilitated by informal communicative cues, like hand gestures, facial expressions, and eye contact. In the near future, however, when semi- and fully autonomous vehicles are introduced into the traffic system, drivers will gradually assume the role of mere passengers, who are casually engaged in non-driving-related activities and, therefore, unavailable to participate in traffic interaction. In this novel traffic environment, advanced communication interfaces will need to be developed that inform pedestrians of the current state and future behavior of an autonomous vehicle, in order to maximize safety and efficiency for all road users. The aim of the present review is to provide a comprehensive account of empirical work in the field of external human-machine interfaces for autonomous vehicle-to-pedestrian communication. In the great majority of covered studies, participants clearly benefited from the presence of a communication interface when interacting with an autonomous vehicle. Nevertheless, standardized interface evaluation procedures and optimal interface specifications are still lacking.

Technology Development of Electric Vehicles: A Review

To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution.

Implementation of Sustainable Motorways of the Sea Services Multi-Criteria Analysis of a Croatian Port System

Motorways of the Sea present a practical example of combined and sustainable transport. With respect to the present model of functionality of the transport system in the Republic of Croatia, where the development of the port and of the gravitational transport system is left to each individual port (six independent ports), and where there is no systematic approach to specialization of each port or route, the results of the analysis are not promising. The goal of this research is to explain why the current Motorways of the Sea implementation model is inefficient and unsustainable. The analysis shows that a scientific approach to multicriteria analysis, which includes the identification of all relevant factors, the analysis of the transport infrastructure, as well as the identification of criteria which could potentially affect the Motorways of the Sea system, can correctly define the optimal model for the implementation of a sustainable Motorways of the Sea system.

A Survey of Security Services, Attacks, and Applications for Vehicular Ad Hoc Networks (VANETs)

Vehicular ad hoc networks (VANETs) are an emerging type of mobile ad hoc networks (MANETs) with robust applications in intelligent traffic management systems. VANET has drawn significant attention from the wireless communication research community and has become one of the most prominent research fields in intelligent transportation system (ITS) because of the potential to provide road safety and precautionary measures for the drivers and passengers. In this survey, we discussed the basic overview of the VANET from the architecture, communication methods, standards, characteristics, and VANET security services. Second, we presented the threats and attacks and the recent state-of-the-art methods of the VANET security services. Then, we comprehensively reviewed the authentication schemes that can protect vehicular networks from malicious nodes and fake messages. Third, we discussed the latest simulation tools and the performance of the authentication schemes in terms of simulation tools, which was followed by the VANET applications. Lastly, we identified the open research challenges and gave future research directions. In sum, this survey fills the gap of existing surveys and summarizes the latest research development. All the security attacks in VANETs and their related countermeasures are discussed with respect to ensuring secure communication. The authentication schemes and comprehensive applications were introduced and analyzed in detail. In addition, open research challenges and future research directions were issued.