Cooperative Intelligent Transport Systems Research Articles

Sectional Information-Based Collision Warning System Using Roadside Unit Aggregated Connected-Vehicle Information for a Cooperative Intelligent Transport System

Vehicular collision and hazard warning is an active field of research that seeks to improve road safety by providing an earlier warning to drivers to help them avoid potential collision danger. In this study, we propose a new type of a collision warning system based on aggregated sectional information, describing vehicle movement processed by a roadside unit (RSU). The proposed sectional information-based collision warning system (SCWS) overcomes the limitations of existing collision warning systems such as the high installation costs, the need for high market penetration rates, and the lack of consideration of traffic dynamics. The proposed SCWS gathers vehicle operation data through on-board units (OBUs) and shares this aggregated information through an RSU. All the data for each road section are locally processed by the RSU using edge computing, allowing the SCWS to effectively estimate the information describing the vehicles surrounding the subject vehicle in each road section. The performance of the SCWS was evaluated through comparison with other collision warning systems such as the vehicle-to-vehicle communication-based collision warning system (VCWS), which solely uses in-vehicle sensors; the hybrid collision warning system (HCWS), which uses information from both infrastructure and in-vehicle sensors; and the infrastructure-based collision warning system (ICWS), which only uses data from infrastructure. In this study, the VCWS with a 100% market penetration rate was considered to provide the most theoretically similar result to the actual collision risk. The comparison results show that in both aggregation and disaggregation level analyses, the proposed SCWS exhibits a similar collision risk trend to the VCWS. Furthermore, the SCWS shows a high potential for practical application because it provides acceptable performance even with a low market penetration rate (30%) at the relatively low cost of OBU installation, compared to the VCWS requirement of a high market penetration rate at a high installation cost.

An efficient path planning GLOSA‐based approach over large scale and realistic traffic scenario

Vehicle speed and trajectories play an important role in influencing fuel consumption, gas emissions and travel time. ITS services are often proposed in order to reduce the impact of these issues on the environment and the comfort of drivers. Cooperative Intelligent Transport Systems (C‐ITS) introduced many services that target traffic efficiency management and road safety. Green Light Optimal Speed Advisory (GLOSA) is one of the traffic efficiency services that can significantly cut fuel consumption and decrease waiting time while crossing intersections. When approaching a signalized intersection, GLOSA informs the driver of an advisory speed to respect in order to reach the intersection while the traffic light is green. In this paper, we propose an Efficient Path Planning GLOSA‐based (EPP‐GLOSA) approach using Dijkstra greedy algorithm and GLOSA service. EPP‐GLOSA aims at proposing the fastest path for the driver to follow in order to reach its destination. The estimated duration to reach the destination takes into consideration the state of traffic lights along the path on top of Dijkstra algorithm. The proposed approach is evaluated under a large scale scenario in free flow and rush‐hour conditions. Our findings show that EPP‐GLOSA offers a balanced scale between travel time reduction and fuel saving especially in free flow conditions.

Cooperative Intelligent Transport Systems (C-ITS) as solutions increasing the safety and efficiency of road transport

Cooperative Intelligent Transport Systems (C-ITS) as solutions increasing the safety and efficiency of road transport

The Need for Cooperative Automated Driving

In this paper we describe cooperation and social dilemmas in multiagent systems, with an analogy applied to road traffic. Cooperative human drivers, based on their perception of trust and fairness, find efficient solutions for such dilemmas. In the development of automated vehicles (AVs) it is therefore important to ensure that this cooperative ability is maintained even without a human driver. Therefore, the topic of cooperative intelligent transport systems (C-ITSs) is discussed in detail and different characteristics of cooperation and their implementation are derived. Further, three planning levels with the corresponding communication techniques are discussed and several methods for maneuver planning are listed. All in all, we hope that this paper will allow us to better classify different cooperative scenarios, develop novel approaches for cooperative AVs (CAVs), and emphasize the need for cooperative driving.

Simulation Framework for Misbehavior Detection in Vehicular Networks

Cooperative Intelligent Transport Systems (C-ITS) is an ongoing technology that will change our driving experience in the near future. In such systems, vehicles and RoadSide Unit (RSU) cooperate by broadcasting V2X messages over the vehicular network. Safety applications use these data to detect and avoid dangerous situations on time. MisBehavior Detection (MBD) in C-ITS is an active research topic which consists of monitoring data semantics of the exchanged Vehicle-to-X communication (V2X) messages to detect and identify potential misbehaving entities. The detection process consists of performing plausibility and consistency checks on the received V2X messages. If an anomaly is detected, the entity may report it by sending a Misbehavior Report (MBR) to the Misbehavior Authority (MA). The MA will then investigate the event and decide to revoke the sender or not. In this paper, we present a MisBehavior Detection (MBD) simulation framework that enables the research community to develop, test, and compare MBD algorithms. We also demonstrate its capabilities by running example scenarios and discuss their results. Framework For Misbehavior Detection (F 2 MD) is open source and available for free on our github.

Enhancing Bus Holding Control Using Cooperative ITS

We introduce a novel hybrid controller, which aims to maintain regularity on bus operations while reducing the number of stops at signalized intersections. The controller combines a rule-based holding criterion with a cooperative intelligent transportation systems based strategy, namely green light optimal dwell time adaptation (GLODTA), which exploits signal phase and timing information to modify dwell times at stops. In order to address the bi-objective criterion, holding control is integrated with transit signal priority (TSP) and an additional driving advisory system, which adjusts speed to reduce stopping at signals. The different controllers are evaluated using simulation of major bus line in Luxembourg City. The results show that buses are efficiently operated without necessarily providing additional priority via TSP, hence without negatively affecting the capacity of the private vehicles system. The new controller improves further the performance in terms of regularity and reduces the number of stops at traffic lights.

Roadworks Warning—Closure of a Lane, the Impact of C-ITS Messages

By now, it is widely acknowledged among stakeholders and academia that infrastructures will have to be composed both by a physical component and a digital one. The deployment of technologies exploiting dedicated short-range communications is viewed as the most cost-effective solution to face the foreseen growth of mobility. Still, little has been done to define the best implementation logic of DSRC. Aim of this paper is to frame the possible impacts arising by the implementation of a cooperative intelligent transport system (C-ITS)-use case: roadworks warning—closure of a lane, and, in order to achieve this result, microsimulations are exploited. The results are intended to support both road operators and car-makers in defining the best operational logics and the possible benefits achievable by presenting the cooperative message at a certain distance for certain market penetrations. Moreover, if the C-ITS message actually entails benefits or simply disrupts the upstream traffic should be assessed in advance, before implementing the system. The obtained results show that the risk of disruption and of reduction in traffic efficiency arises at lower market penetration levels. Nevertheless, a consistent trend in delay reduction is recorded upstream the roadworks, the highest reduction being equal to 8.66%. Moreover, the average speed at the roadworks entrance on the closing lane increases by a difference equal to around 10 km/h, while the average time in the queue at the highest market penetration reduces by 60 s on the open lane and 25 s on the closing one. These presented results reflect the way the traffic shifts from the slow to the fast lane thanks to the C-ITS system and effectively frames both the potentialities and the risks of the system.

Pore acceptance predictions of motorised Two-Wheelers during filtering at urban Mid-Block sections

Filtering of motorized two-wheelers (MTWs) is a common practice in dense urban heterogeneous traffic environments where they often tend to navigate through the available lateral spaces (commonly termed as ‘pore’ in the literature) described by the vehicles in-front. Considering the increased vulnerability of MTW riders in dense urban systems, proper evaluation and modeling of pore acceptance/rejection predictions of MTWs can essentially provide a safer driving environment to MTW riders and the surrounding vehicles in a cognitive architecture, augment the reliability and predictability of microsimulation models and ameliorate the overall traffic flow phenomena. The current study therefore investigates the applicability of Raff’s method, binary logit model and support vector machines (SVM) in predicting the pore acceptance decisions of MTW riders during filtering in urban mid-block sections. The results of the study suggested that SVM technique could be considered as a potential tool for estimating the pore acceptance and rejection predictions of MTW riders, which can be further implemented in a cooperative intelligent transport systems environment for an overall safe yet smooth flow of traffic.

A framework for testing independence between lane change and cooperative intelligent transportation system.

Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. We are preparing for the largest Field Operational Test (FOT) in Australia to evaluate C-ITS safety benefits. Two of the safety benefit hypotheses we formulated assume a dependency between lane changes and C-ITS warnings displayed on the Human Machine Interface (HMI) during safety events. Lane change detection is done by processing many predictors from several sensors at the time of the safety event. However, in our planned FOT, the participating vehicles are only equipped with the vehicle C-ITS and the IMU. Therefore, in this paper, we propose a framework to test lane change and C-ITS dependency. In this framework, we train a random forest classifier using data collected from the IMU to detect lane changes. Consequently, the random forest output probabilities of the testing data in case of C-ITS and control are used to construct a 2x2 contingency table. Then we develop a permutation test to calculate the null hypothesis needed to test the independence of the lane change during safety events and the C-ITS.

Use Of Smartphones for Ensuring Vulnerable Road User Safety through Path Prediction and Early Warning: An In-Depth Review of Capabilities, Limitations and Their Applications in Cooperative Intelligent Transport Systems.

The field of cooperative intelligent transport systems and more specifically pedestrians to vehicles could be characterized as quite challenging, since there is a broad research area to be studied, with direct positive results to society. Pedestrians to vehicles is a type of cooperative intelligent transport system, within the group of early warning collision/safety system. In this article, we examine the research and applications carried out so far within the field of pedestrians to vehicles cooperative transport systems by leveraging the information coming from vulnerable road users’ smartphones. Moreover, an extensive literature review has been carried out in the fields of vulnerable road users outdoor localisation via smartphones and vulnerable road users next step/movement prediction, which are closely related to pedestrian to vehicle applications and research. We identify gaps that exist in these fields that could be improved/extended/enhanced or newly developed, while we address future research objectives and methodologies that could support the improvement/development of those identified gaps.

A hybrid integrity monitoring method using vehicular wireless communication in difficult environments for GNSS

Vehicular communication using the DSRC (Dedicated Short Range Communication) standard is becoming an enabling factor for a wide range of Cooperative Intelligent Transportation System (C-ITS) applications, especially those with high requirements of safety assurance. The location information of connected vehicles is fundamental, and significant for ensuring safe driving and preventing traffic accidents under complex and uncertain conditions. In order to alleviate the drawbacks of GNSS (Global Navigation Satellite System) based positioning due to satellite signal non-availability under difficult environments such as dense urban areas, a hybrid integrity monitoring technique is proposed. The range-rate measurement from the DSRC unit in the vehicle is employed to assist integrity monitoring of observable satellites, and the DSRC measurements are sent to the target vehicle through a vehicle-to-vehicle channel along with the identified state of neighboring vehicles. With the assistance from surrounding neighborhoods and a road map database, a virtual satellite ranging solution is possible to expand the measurements for enabling autonomous GNSS integrity monitoring and fault detection without the limitation of satellite visibility. Simulations conducted with different vehicle operation conditions and GNSS fault models demonstrate that the proposed solution outperforms conventional RAIM (Receiver Autonomous Integrity Monitoring) methods making it promising for many location-based cooperative applications.

A review of traffic signal control methods and experiments based on Floating Car Data (FCD)

This paper intends to give a short review of the state of the art on the use of floating car data concerning the management of traffic flow at signalized intersections. New technologies such as connected and autonomous vehicles and Co-operative Intelligent Transportation Systems (C-ITS) are going to change the future of traffic control and management. Traffic signal control systems can be reorganized by using Floating Car Data (FCD), yet the concept of floating car data (FCD) has been mainly studied to gain traffic information and/or signal information. Only recent works have been focalizing on the potential application of FCD for traffic signal real-time control. This paper aims to evidence the most important concepts that can be extracted from the literature on this important topic.

Road traffic optimisation based on a learning approach

Road traffic management is an important issue for authorities, road operators and users. Many researchers have worked since many years in order to propose smart solutions. Some of them have been adopted by road operators and applied on road signalling or on traffic light management. During the last decade, cooperative intelligent transport systems (C-ITS) have emerged and are being deployed. They permit on one hand to increase the number of connected vehicles and on another hand to deploy road side units (RSU) along roads and junctions. RSUs together with central road operator servers compose the road infrastructure used for infrastructure to vehicle communications (I2V). RSUs disseminate continuously messages about road safety and traffic flows to vehicles. Vehicles and RSUs are able to measure, to compute, and to disseminate relevant information. In this study, we add to these actors the ability to learn road behaviours in order to be able to provide the optimal path in terms of travel time for vehicles seeking to reach their destination. The solution is based on a reinforcement learning technique, in particular the Q-learning, that is used to learn the best action to take into account according to various situations. The transit delay from one location to another is used to evaluate the rewards. The simulation results confirm that the proposed Q-learning approach outperforms the existing greedy algorithm with better performances in terms of a transit delay.

Road traffic optimisation based on a learning approach

Road traffic management is an important issue for authorities, road operators and users. Many researchers have worked since many years in order to propose smart solutions. Some of them have been adopted by road operators and applied on road signalling or on traffic light management. During the last decade, cooperative intelligent transport systems (C-ITS) have emerged and are being deployed. They permit on one hand to increase the number of connected vehicles and on another hand to deploy road side units (RSU) along roads and junctions. RSUs together with central road operator servers compose the road infrastructure used for infrastructure to vehicle communications (I2V). RSUs disseminate continuously messages about road safety and traffic flows to vehicles. Vehicles and RSUs are able to measure, to compute, and to disseminate relevant information. In this study, we add to these actors the ability to learn road behaviours in order to be able to provide the optimal path in terms of travel time for vehicles seeking to reach their destination. The solution is based on a reinforcement learning technique, in particular the Q-learning, that is used to learn the best action to take into account according to various situations. The transit delay from one location to another is used to evaluate the rewards. The simulation results confirm that the proposed Q-learning approach outperforms the existing greedy algorithm with better performances in terms of a transit delay.

The use of a Blockchain-based System in Traffic Operations to promote Cooperation among Connected Vehicles

This paper intends to present some ideas for the implementation of cooperative ITS systems based on the Blockchain Technology (BT) concept. Blockchain technology has been recently introduced and, in this paper, we discuss a system that is based on a dedicated blockchain, able to involve both drivers and city administrations in the adoption of promising and innovative technologies that will create cooperation among connected vehicles. The proposed blockchain-based system can allow city administrators to reward drivers when they are willing to share travel data. The system manages in a special way the creation of new coins which are assigned to drivers and institutions participating actively in the system. Moreover, the system allows keeping a complete track of all transactions and interactions between drivers and city management on a completely open and shared platform. The main idea is to combine connected vehicles with BT to promote Cooperative ITS use and a better use of infrastructures.

Deep Learning Based Channel Estimation Schemes for IEEE 802.11p Standard

IEEE 802.11p standard is specially developed to define vehicular communications requirements and support cooperative intelligent transport systems. In such environment, reliable channel estimation is considered as a major critical challenge for ensuring the system performance due to the extremely time-varying characteristic of vehicular channels. The channel estimation of IEEE 802.11p is preamble based, which becomes inaccurate in high mobility scenarios. The major challenge is to track the channel variations over the course of packet length while adhering to the standard specifications. The motivation behind this paper is to overcome this issue by proposing a novel deep learning based channel estimation scheme for IEEE 802.11p that optimizes the use of deep neural networks (DNN) to accurately learn the statistics of the spectral temporal averaging (STA) channel estimates and to track their changes over time. Simulation results demonstrate that the proposed channel estimation scheme STA-DNN significantly outperforms classical channel estimators in terms of bit error rate. The proposed STA-DNN architectures also achieve better estimation performance than the recently proposed auto-encoder DNN based channel estimation with at least 55.74% of computational complexity decrease.

Cellular and Short-Range Communication: The Best of Both Worlds

The legal provisions of Cooperative Intelligent Transport Systems were planned to enter the next phase, but the EU Council voted down the correspondingly proposed Delegated Act. Amongst others, the exclusively chosen Wifi-p communication technology was not acceptable from a data protection perspective while the telecom sector was shut out to provide data-transmission services for C-ITS. This paper presents an analysis of technologies, including cellular technology, for car-to-car communication within C-ITS, which shows that the data protection flaws in C-ITS can be remedied by using cellular technology instead of Wifi-p, or by allowing encrypted C-ITS messages over Wifi-p and cellular technology to preload the required encryption and decryption keys.<br/>

IEEE 802.15.7-Compliant Ultra-Low Latency Relaying VLC System for Safety-Critical ITS

The integration of Visible-Light Communications technology (VLC) in Intelligent Transportation Systems (ITS) is a very promising platform for a cost-effective implementation of revolutionary ITS and cooperative ITS protocols. In this paper, we propose an infrastructure-to-vehicle-to-vehicle (I2V2V) VLC system for ITS, implementing it through a regular LED traffic light serving as a transmitter and a digital Active Decode-and-Relay (ADR) stage for decoding and relaying the received information towards further incoming units. The proposed VLC system targets the challenging and important case of ultra-low latency ADR transmission of short packets, as this is needed for emerging applications of automatic braking, car platooning and other critical automatic and/or assisted driving applications. The experimental validation of the ADR VLC chain, as well as a thorough statistical analysis of errors distribution in the transmission, has been performed for short to medium distances, up to 50 meters. The performances of the designed system are evaluated by measuring the packet error rate (PER) and latency in the whole ADR transmission chain. Our analysis shows that our system attains ultra-low, sub-ms latencies at 99.9\% confidence level for PER as high as $5\times10^{-3}$, yet granting a latency below 10 ms even for distances of 50 m. The demonstrated system prototype is compatible with IEEE 802.15.7 standard.

Broadcast Authentication in Latency-Critical Applications: On the Efficiency of IEEE 1609.2

Standards such as the American IEEE 1609, European ETSI ITS-G5, and Japanese ARIB STD-T109 aim to establish Cooperative Intelligent Transportation Systems (C-ITS) by enabling Vehicular Ad-Hoc Networks (VANETs). In VANETs, vehicles communicate with other vehicles and roadside infrastructure to support latency-critical applications which increase driver awareness of the surroundings. This should result in improved safety and possibly optimizing traffic. However, to secure VANET communications against message manipulation or replaying, security standards such as IEEE 1609.2 and ETSI TS 103 097 are proposed. In this work, we implement the cryptographic primitives recommended in the IEEE 1609.2 standard to authenticate low latency safety critical messages. We evaluate the effect of the implementation using metrics such as CPU clock cycles per operation, average computation time in milliseconds, and message size in bits. We perform a simulation presenting a high-density highway scenario for the above mentioned C-ITS standards. For each standard, we evaluate the number of safety messages that can be successfully received within 100 ms latency. We show how and to what extent the authentication overhead of latency-critical messages may impact on driver safety. Under an assumed traffic scenario, we show that a crash is possible, as a result of the evaluated authentication delay. We show that the recommended algorithms with specific parameters can be a potential solution for low latency safety-critical applications in a large scale scenario.

On the Edge of the NIS Directive: The Proposed C-ITS Delegated Regulation, Friend or Foe?

The paper discusses the interpretation of the lex specialis rule of Article 1 (7) of the NIS Directive, which has deserved little scholarly attention so far. Article 1 (7) NIS Directive organises the interface between the NIS Directive regime and other European Union sector-specific legislations imposing (cyber)security obligations, by laying down the conditions according to which such obligations would prevail over the NIS Directive regime. To do so, the recent proposal from the European Commission to regulate Cooperative Intelligent Transport Systems (‘C-ITS’) serves as a practical case study. Even though the proposal was eventually turned down by the Council, the regulation of C-ITS remains top on the agenda of the European Commission. Its relevance for the present study also detracts from the fact that, while obviously dealing with (cyber)security, the Proposed C-ITS Regulation barely envisaged the interface with the NIS Directive. The paper finds that the NIS Directive is unclear on a number of aspects related to its interface with other EU legal frameworks, and notably in the field of ITS. In anticipation of a potential future C-ITS regulation, the paper makes suggestions to smoothly regulate the interface with the NIS Directive and eventually bring legal certainty to the actors involved. Finally, recommendations are made with respect to the interpretation and application of the lex specialis rule of Article 1 (7) NIS Directive.