Visible light communications in intelligent transportation systems

There is great concern over growing road accidents and associated fatalities. In order to reduce accidents, improve congestion and offer smooth flow of traffic, several measures, such as providing intelligence to transport, providing communication infrastructure along the road, and vehicular communication, are being undertaken. Traffic safety information broadcast from traffic lights using Visible Light Communication (VLC) is a new cost effective technology which assists drivers in taking necessary safety measures. This chapter presents the VLC broadcast system considering LED-based traffic lights. It discusses the integration of traffic light Roadside Units (RSUs) with upcoming Intelligent Transportation Systems (ITS) architecture. Some of the offered services using this technology in vehicular environment together with future directions and challenges are discussed. A prototype demonstrator of the designed VLC systems is also presented.

Traffic safety information broadcast from traffic lights using Visible Light Communication (VLC) is a new cost effective technology which can draw attention to drivers to take necessary safety measures. This paper presents a VLC broadcast system considering LED-based traffic lights. It discusses the conceptual methodology for integrating traffic light road side unit (RSUs) with impending intelligent transportation systems (ITS) architecture. Results from a case study of VLC system for information broadcast are also presented.

This paper presents fog-based intelligent transportation systems (ITS) architecture for traffic light optimization. Specifically, each intersection consists of traffic lights equipped with a fog node. The roadside unit (RSU) node is deployed to monitor the traffic condition and transmit it to the fog node. The traffic light center (TLC) is used to collect the traffic condition from the fog nodes of all intersections. In this work, two traffic light optimization problems are addressed where each problem will be processed either on fog node or TLC according to their requirements. First, the high latency for the vehicle to decide the dilemma zone is addressed. In the dilemma zone, the vehicle may hesitate whether to accelerate or decelerate that can lead to traffic accidents if the decision is not taken quickly. This first problem is processed on the fog node since it requires a real-time process to accomplish. Second, the proposed architecture aims each intersection aware of its adjacent traffic condition. Thus, the TLC is used to estimate the total incoming number of vehicles based on the gathered information from all fog nodes of each intersection. The results show that the proposed fog-based ITS architecture has better performance in terms of network latency compared to the existing solution in which relies only on TLC.

Globally, intelligent vehicles and telematics R&D through the integration of IT technologies in the vehicle are significant increasing. Real-time data communication for intelligent transportation system (ITS) is very important. It collects real-time data from the vehicle and provides the information collected from ITS center. We propose an effective and secure communication scheme for these communication procedures. In particular, our proposed SIP-based MVPN reduces signaling cost and has many advantages in security aspects. In addition, our proposed scheme performs the mobility management applying Network Mobility (NEMO) for the communication between the vehicles. In other words, we propose an ITS communication mechanism of SIP-based mobile VPN and V2V NEMO. Finally, our performance analysis show that the ITS of SIP-based MVPN is significantly reducing the handoff signaling cost.

Problem: Services that use ICT (Information and Communications Technologies) have been developed to improve the mobility in cities; however, especially in developing countries, these services are not often based on adequate reference architectures, such as ITS (Intelligent Transport Systems) architectures, which prevent integration and interoperability. Objective: Propose a development process for the design of mobility services in an intermediate city of a developing country, based on an ITS architecture formulated particularly considering context. Methods: The reference ITS architectures and the particular context of a Colombian intermediate city are reviewed, in order to identify which is the best process to adapt an ITS architecture to these type of cities. With the process identified, the ITS architecture for Popayan (Colombian intermediate city) is designed and finally, the design of the services based on it, is carried out. Results: The methodology developed for the design of the ITS architecture and the architecture designed particularly for Popayan are summarized. Following, the design of two mobility services (“Public transport vehicle tracking” and “Traffic measurement”) for that city based on architecture is detailed. Conclusions: The particular environment of an intermediate city and its priorities allows to determine the services to select for its ITS architecture from a reference architecture. ITS architecture development of a city allows the incremental development of services that really improve their mobility in a sustainable manner.

Different wireless communication technologies, such as wireless fidelity (Wi-Fi), 4G cellular technologies and worldwide interoperability for microwave access (WiMAX) have been used as alternatives or supplement to wired communication in intelligent transportation systems (ITS). Widespread deployment of wireless technologies in ITS require a comprehensive understanding of their performance, limitations and advantages in different field conditions. The authors evaluated performance of Wi-Fi wireless communication between adjacent roadside ITS devices (i.e., nodes) in different field conditions with varying characteristics of Wi-Fi technology. Field tests revealed that modulation rates, transmission power, line of sight, distance between nodes play critical roles in the performance of Wi-Fi communication in different roadway conditions. To achieve a desired level of performance requirements between adjacent nodes, minimum network performance thresholds must be realized in the field. Transportation agencies can identify the achievable performance, such as saturated throughput, delivery ratio and received signal strength at a particular location, by following the field test procedure developed in this research. The evaluation strategies and results presented in this study will contribute to the future planning and design of Wi-Fi communication for a roadway wireless sensor or device network considering corresponding communication performance requirements for specific ITS applications.

The paper presents an analytic approach to generate the market packages for the operation via the intelligent transportation system (ITS) architecture. This analytic approach uses the concepts of the "use case diagram" and "sequence diagram" defined by the unified modeling language (UML). By following the steps, we take Taiwan's public bus service as an example and generate the market package effectually, public bus monitoring and public bus scheduling and dispatching, which is corresponding to the real world situation. We also point out the academic models in market packages that could be the patent products. Consequently, driving the establishment of ITS market package not only conduces to ITS development but also promotes the knowledge economic in our society.

The presence of line-of-sight (LOS) and non-line-of-sight conditions in an intelligent transportation system (ITS) is critical, as it directly impacts the localisation and overall system performance. Distinguishing LOS, first-order and higher-order multipaths (HOMP) is hindered by the dense multipath (MP) propagation. The computational resource requirement and limited accuracy associated with traditional iterative methods have stimulated the use of deep learning (DL). This work proposes a novel, lightweight, dual limb attention (DLA) based DL network termed as “MOVENetx64”, for robust identification of MPs in ITS. DLA mechanism stacked with convolutional neural networks and long-short-term memory layers forms the prominent feature extractor, which enables feeding raw, unprocessed data into the DL pipeline. A novel alternating loss strategy is designed to nullify the effect of the extreme imbalance associated with the target output classes. Ray-tracing-based vehicular datasets for City, Suburban, and Highway are generated. A combination of time, received power, angular characteristics, and phase spectrum forms the input feature-set. The proposed MOVENetx64 showcases MP classification accuracy of (98.71%,99.41%,96.44%), and achieves the least HOMP prediction error of (0.6%,0.17%,2.28%) on the datasets. These results validate that the proposed MOVENetx64 is scalable and computationally efficient for distinguishing multipaths efficiently and enabling reliable vehicular communication in ITS.

The Intelligent Transport System(ITS) is an important development in this technological era. Effective communication in ITS is possible by means of different network systems and technologies, and as a result, driverless vehicles are becoming the target of cyber-attacks. The CAN bus is mainly used in ITS for internal communication and is vulnerable to cyber-attacks. CAN protocol gives minimal information for the design of intelligent algorithms for detecting intruders. The existing intrusion detection mechanisms are computationally expensive and may not be suitable for low-end ECUs in vehicles. In this work, we have given importance to the data part of the frame while preparing the dataset for the experiments. We have explored the performance of several classical machine learning algorithms like Random Forest algorithm, XGBoost algorithm, LightGBM algorithm, Naive Baye’s algorithm, and Decision Trees with the refined dataset and used the publically available dataset ’Car Hacking: Attack and Defence Challenge’ for the experimental evaluation. In the results, the Random Forest algorithm got the most significant accuracy with 95% and an F1-score of 95%. Compared to the computationally complex algorithms, we could achieve comparable and significant results with the classical machine learning algorithms which can be easily portable to ECUs in vehicles.KeywordsIntrusion detectionController area networkRandom forest algorithm.Lightweight intrusion detection

This work presents a functionality based model for survivable intelligent transportation system (ITS) control infrastructures. Specifically, a method is shown that allows us to determine the vulnerability of an ITS communication infrastructure to malicious acts and to derive optimal mitigations. The research is applied to a real ITS currently being implemented in a small town. Results show that significant reductions in vulnerability can be achieved utilizing the principle of redundancy with minimal effort. The method presented is especially suitable to reducing vulnerabilities of existing ITSs as it can be effectively applied after an ITS has been built. Mitigation techniques can be derived based on maximal benefit under consideration cost-benefit ratio.

Worldwide intelligent transportation systems (ITSs) are at the edge from research and development to deployment and commercial operation. Japan has already introduced ITS technology in the market and the USA and the European Union have elaborated planes for deployment. ITS in this context means communication and services based on communication technology between vehicles and other traffic participants on one hand and traffic and road infrastructure, vehicle manufacturer, and other mobility service provider on the other hand. For a successful introduction, a reliable and secure exchange of mobility‐related information is a key factor. To provide such an exchange for an overall architecture for ITS and for all participants and users is necessary. With the introduction of the market, the assessment of an ITS architecture without an existing reference system arises. Since most assessment methods currently existing, measure how ‘good’ an architecture is in comparison with another architecture. In this study, the authors describe ways and approaches how existing methods can be extended and combined to provide means for the assessment of ITS architectures in the pre‐deployment phase. As a result, deploying parties should be enabled to assess an architecture before introducing it to the public.

The paper analyzes the role of a complete architecture of intelligent transport systems (ITS) in ensuring their effectiveness. Many countries or groups of countries have ITS architecture with successful results of its application in practice. It is shown that the ITS architecture is constantly being updated and this is a characteristic trend. The paper proposes new approaches to the formation of the architecture of the integration platform within ITS. The scheme of organisation of data flows in different loops of software tools of the integration platform is developed. It is proposed to separate the control loops proper transport system data and identification data, which describe a partisipant of the ITS (digital object) with the expansion of capabilities in a cooperative ITS. It is possible to organise linked chains of identification of ITS digital objects, to participate in the formation of network structures for ITS work processes, describing the behaviour of digital objects during ITS processes and the behaviour of end users in accordance with the properties of network structures.

This paper presents a method for performing traffic light detection using computer vision. Reliable traffic light detection and classification is crucial for automated driving in urban environments. By using big data and artificial intelligence, a complex dataset belonging to an urban area in China is preprocessed to determine the level of vehicular congestion, and then different machine learning algorithms are applied to a dataset of traffic light images in order to validate them in the urban environment to be studied, this process is explained step by step. The models obtained in this work can be applied in optical camera communication (OCC) systems, and also in intelligent transportation systems (ITS), using tracking channels for visible light communication (VLC). The two optical channels, VLC and OCC, are simulated in terms of the quality of information received in order to apply the previously generated datasets. In this work, a traffic light feature dataset has been generated from images and two traffic light classification models present in images and video frames have been generated from their features, obtaining a maximum accuracy of 94.52 %.

With the rapid maturation in automotive industry, intelligent sensing has been emerged as a vital research field for fast paced future intelligent transportation system. After the progressive integration of advanced driver assistance system and the rapid growth in next generation Internet of Vehicles (IoV), diver’s identification constitutes an indispensable aspect for the authorization in next generation internet-connected smart vehicles. Real-time driver identification is crucial in next generation smart vehicles. The presented solution is based on channel state information (CSI) of WiFi signals. In this proposed framework, an innovative WiFi- based low cost driver identification system is proposed which can recognize driver with good accuracy and less computational burden. The core idea is based on analyzing the steering wheel maneuver, exploiting CSI of WiFi signal. In the era of advanced information and communication in intelligent transportation system, this driver identification system may incorporate various intelligent means that are beneficial in many applications including safety, security, fleet management, ride hailing, insurance telematics, and customized vehicles. The proposed framework can recognize the activities with an average accuracy of 92.5% and identification with an average accuracy of 91.8%.

In this work we present solutions which aim at enhancement of the localization precision of the road side unit (RSU) devices which will participate in vehicle-to-infrastructure (V2I) communication in future autonomous driving and intelligent transportation systems (ITS).Currently used localization techniques suffer from limited accuracy which is due to various factors, including noise, delays caused by environmental conditions (e.g.temperature variation) and differences in elevation between devices communicating with each other in the road environment.In case of application of the ITS, these factors can be the source of significant discrepancies between real positions of the RSUs and their estimated values provided by the V2I system.The proposed techniques, based on various approximation techniques, as well as linear and nonlinear filters, allow to improve the localization accuracy, reducing the positioning errors by more than 90 %.