Cooperative Intelligent Transport Systems Research Articles

Robust Beam Management in Position and Velocity Aware V2V Communications Using Distributed Antenna Subarrays

Millimeter-wave vehicle-to-vehicle communication systems with multiple-input multiple-output antenna array have drawn significant attention because it is expected to satisfy the growing bandwidth requirements for the cooperative intelligent transportation systems. However, maintaining high-quality communication links between communicating vehicles without high overhead is a challenging problem due to the high mobility of vehicles. In this paper, we propose a beam management algorithm with spatially distributed antenna subarrays instead of a single co-located antenna array. Unlike existing methods, we use distributed antenna subarrays to exploit the geometric relationship of the beam directions and the relative distance between communicating vehicles. We reduce the beam alignment errors by minimizing the sum of squared errors between the estimated beam direction after the beam training process and the refined beam direction derived from the measured position and velocity data. Additionally, a position data request protocol is devised to reduce the beam refining errors. Numerical results demonstrate that the proposed beam management algorithm successfully reduces the beam alignment errors and it is robust to position and velocity measurement errors.

Real-time traffic impedance and priority based cooperative path planning mechanism for SOC-ITS: Efficiency and equilibrium

With the support of new information technologies such as the Internet of Vehicles (IoV) and cloud computing, the Intelligent Transportation System (ITS) has begun a new stage role as Cooperative ITS (C-ITS). Moreover, featuring the collaboration of vehicles, roads, clouds and humans, it becomes possible to scientifically mitigate several serious phenomena in the traditional ITS, including traffic congestion, blocked emergency vehicles, etc. While focusing on optimizing the Quality of Transportation Service (QoTS) requirements from vehicles with different types of tasks, covering emergency vehicles (fire engine, police vehicle and ambulance), shuttle buses, and others, this paper studies and proposes a new service-oriented and cloud-based cooperative path planning mechanism for such heterogeneous vehicles. First, the architecture of service-oriented cooperative path planning is established, where the types and QoTS of traffic tasks are illustrated. Then, a hybrid traffic impedance model is put forward, in which three influencing factors of traffic impedance are concluded and combined through the entropy weight method. On this basis, a service-oriented cooperative path planning mechanism with load balancing strategies named SoCPPLB is designed. The main principle of SoCPPLB is to provide different levels of path planning service for vehicles according to their QoTS requirements per the constraint of the QoTS of vehicles and traffic equilibrium. Finally, all these proposed models and algorithms have been implemented within the Simulation of Urban MObility (SUMO), and further verified under several typical traffic scenarios. The experimental results show that, this new mechanism can effectively improve the QoTS of emergency vehicles and traffic equilibrium performance obviously.

The Coronavirus Pandemic (COVID-19) and Cooperative-Intelligent Transport Systems

Cooperative-intelligent transport systems (C-ITS) are a solution for the manifold challenges of traffic as well as a major competitive stepping stone for the European automotive and tele-communication industry. However, against the backdrop of far-reaching COVID-19 impacts, the management of such complex C-ITS megaprojects needs to be adapted. Insights from most recent research contributions on megaproject management and the unexpected have shown that fostering a common sense of action, intense and efficient communication, and a strong organizational culture can help C-ITS projects to cope with the unprecedented situation. The institutional role model has proven to be an effective implementation framework for employing such project management methods in the C-ITS context. By combining different strands of research in an analysis of the intersection between recent C-ITS findings and theory on megaproject management and the unexpected, this article contributes to a holistic understanding of the impact of COVID-19 on C-ITS.

PKIs in C-ITS: Security functions, architectures and projects: A survey

In the smart cities context, Cooperative Intelligent Transportation Systems (C-ITS) represent one of the main use cases that aim to improve peoples' daily lives. Within these environments, messages are exchanged continuously. The latter must be secure and must ensure users' privacy. In this regard, Public Key Infrastructures (PKIs) represent the major solution to meet security needs. In this work, we present a holistic survey that describes all the different functions and services of a C-ITS PKI and focus on the different standards and consortia works that have been adopted to regulate such PKIs. Relying on the survey, we highlight the main research problems and open challenges for ITS PKIs. Then, we propose a generic model for a C-ITS PKI architecture.

Is it Really Easy to Detect Sybil Attacks in C-ITS Environments: A Position Paper

In the context of current smart cities, Cooperative Intelligent Transportation Systems (C-ITS) represent one of the main use case scenarios that aim to improve peoples’ daily lives. Thus, during the last few years, numerous standards have been adopted to regulate such networks. Within a C-ITS, a large number of messages are exchanged continuously in order to ensure that the different applications operate efficiently. However, these networks can be the target of numerous attacks. The sybil attack is among the most dangerous ones. In a sybil attack, an attacker creates multiple identities and then disguises as several fake stations in order to interfere with the normal operations of the system or profit from provided services. We analyze recently proposed sybil detection approaches regarding their compliance with the current C-ITS standards as well as their evaluation methods. We provide several recommendations such as network and attack models as well as an urban and highway datasets that can be considered in future research in sybil attack detection.

Real-Time Fault Diagnosis for EVs With Multilabel Feature Selection and Sliding Window Control

Real-time fault diagnosis on vehicles can effectively avoid potential accidents, which, however, is difficult and challenging to be widely deployed due to the low computational capability and limited data storage of electric vehicles (EVs). To address this issue, we propose a vehicle-mounted fault diagnosis system with low computational complexity and small data storage, for achieving real-time monitoring of vehicle status. To facilitate the accurate and optimized feature selection, we had been collecting 6.52-GB real data from three EVs in 12 months. Motivated by those data, we first propose a multilabel feature selection algorithm to obtain the feature weights, based on which the optimal number of features is then calculated through the backpropagation neural network (BPNN), thus minimizing the computational cost of real-time fault diagnosis regarding sample dimensions. To further simplify the fault diagnosis system, i.e., reducing the minimum required capacity of data storage, we design a real-time diagnosis sliding window (RDSW) where the window moves forward as new samples arrive and the stale data outside the window are discarded. In particular, we calculate the optimal size of RDSW, which controls the minimum required number of samples to guarantee the accuracy of real-time fault diagnosis. Owing to the mechanism of RDSW, vehicles no longer need to store massive data to guarantee the accuracy of real-time fault diagnosis. In addition, the results of real-time fault diagnosis at each vehicle can be shared with other vehicles in cooperative intelligent transportation systems (C-ITS). Finally, comprehensive simulation is conducted to validate the effectiveness of the proposed diagnosis system in terms of accuracy, complexity and storage capacity.

Pre-Predictive Congestion-Based Data Allocation for Sixth Generation Cooperative Intelligent Transportation Systems

Cooperative intelligent and autonomous transportation systems rely on intelligent sensing, computing, and actuating technologies for unmanned freight and public movements. The information gained from neighbors and communication infrastructures provides efficient actuation for safe and sustained transportation. This article resolves traffic data management congestion using sixth-generation (6G) communication and computing techniques. Terahertz and machine-type communications are exploited for swift information exchange, bypassing the congestion effects. Congestion occurs when demand for road space exceeds supply. This proposal incorporates prediction-based learning to compute the feasibility of handling traffic information and cooperative intelligent transportation. This model is named Congestion-aware Pre-predictive Data Allocation (CPPDA). The traffic flows causing congestion in the data exchange process are predicted for re-allocation and independent channel utilization. In this learning, the pre-predicted instances are updated with the actual identified utilization-to-congestion rate. Therefore, the congestion-causing channels for sensing are identified with ease, reducing the outage. The outage is examined for a basic inter-vehicle data link. Through the optimal allocation of channels for actuation, cloud-aided resources are utilized to a maximum level, leveraging infrastructure support. In addition to an outage of 10.83%, the response time of 14.75%, congestion factor of 8.2%, computational overhead of 6.4%, and information gain factors of 6.86% are analyzed through a comparative study.

A method for predicting ITS cooperative applications performances

Vehicular cooperation taking place in Intelligent Transportation Systems (ITS) aims to solve traffic issues or to improve road safety for vehicles and vulnerable road users. Such cooperation is achieved through distributed applications running in the vehicular network (VANET), composed of communicating units in vehicles and infrastructure. For safety and legal reasons, it is expected that ITS services reach a satisfactory level of efficiency in the context they are deployed. However, predicting the behavior of distributed applications running in dynamic topologies remains a complex problem, which limits their deployment. In this paper, we propose a method able to predict the performances of cooperative ITS (C-ITS) applications in dynamic vehicular networks. It concerns applications relying on messages exchanges between close vehicles or between vehicles and infrastructure. The proposed method is based on an analysis of the application with respect to the vehicular traffic for determining the conditions ensuring the success of its underlying distributed algorithms and protocols. This is formalized as performances properties depending on the network dynamic. It becomes then easy to check whether these conditions are fulfilled or not in a given road traffic, either captured (and anonymized) for an existing road or expected for a new one. Thanks to an appropriate modeling, the conditions are sought and the performances of the C-ITS applications are predicted. We believe that our approach fills a gap between formal studies, VANET simulations and road tests. It will be of particular interest for the deployment of C-ITS applications. This is illustrated by several use-cases studied in realistic conditions.

Awareness on Present and Future Trajectory of Vehicle Using Multiple Hypotheses in the Mixed Traffic of Intersection

In the transition period, autonomous vehicles are mixed with unconnected traffic occupants, such as non-autonomous vehicles and pedestrians, resulting in a major hurdle toward autonomy in urban areas, especially at intersections. In this context, the cooperative-intelligent transportation system (C-ITS) affords a promising solution to achieve a breakthrough with its omniscient sensors network and computing capability. From the perspective of a C-ITS-based service, the trajectory of non-autonomous vehicle is a critical uncertainty that resides at the intersection. Therefore, this paper proposes a unique interactive framework, which is installed in the edge server of C-ITS and can estimate the present trajectories and predict the future trajectories of the non-autonomous vehicles at intersections. The proposed framework was based on multiple hypotheses of possible maneuvers that formed the confined prior set to reduce the high uncertainties posed by the complicated environment of the urban intersection. The resulting all-in-one framework provided a stable long-term trajectory prediction with intrinsic maneuver classification and improved tracking in an integrated way by incorporating the interactions between the multiple hypotheses. This situation awareness can assist autonomous vehicles to drive safely and defensively. The proposed framework was verified using a dataset collected at a real urban intersection.

Benchmarking 4G and 5G-Based Cellular-V2X for Vehicle-to-Infrastructure Communication and Urban Scenarios in Cooperative Intelligent Transportation Systems

Vehicle-to-Infrastructure (V2I) communication is expected to bring tremendous benefits in terms of increased road safety, improved traffic efficiency and decreased environmental impact. In 2017, The 3rd Generation Partnership Project (3GPP) released 3GPP Release 14, which introduced Cellular Vehicle-to-Everything communication (C-V2X), bringing Vehicle-to-Everything (V2X) communication capabilities to cellular networks, hence creating an alternative to Dedicated Short-Range Communications (DSRC) technology. Since then, every new 3GPP Release including Release 15, a first full set of 5G standards, offered V2X capabilities. In this paper, we present a complex simulation study, which benchmarks the performance of LTE-based and 5G-based C-V2X technologies deployed for V2I communication in an urban setting. The study compares LTE and 5G deployed both in the Device-to-Device in mode 3 and in infrastructural mode. Target performance indicators used for comparison are average end-to-end (E2E) latency and Packet Delivery Ratio (PDR). The performance of those technologies is studied under varying communication conditions realized by a variation of vehicle traffic intensity, communication perimeter and message generation frequency. Furthermore, the effects of infrastructure deployment density on the performance of selected C-V2X communication technologies are explored by comparing the performance of the investigated technologies for three infrastructure density scenarios, i.e., involving two, four and eight base stations (BSs). The performance results are put into a context of the connectivity requirements of the most popular V2I communication services. The results indicate that both C-V2X technologies can support all the considered V2I services without any limitations in terms of the communication perimeter, traffic intensity and message generation frequency. When it comes to the infrastructure density deployment, the results show that increasing the density of the infrastructure deployment from two BSs to four BSs offers a remarkable performance improvement for all the considered V2I services as well as investigated technologies and their modes. Further infrastructure density increase (from four BSs to eight BSs) does not yield any practical benefits in the investigated urban scenario.

A Kamm's Circle-Based Potential Risk Estimation Scheme in the Local Dynamic Map Computation Enhanced by Binary Decision Diagrams.

Autonomous vehicles (AV) are a hot topic for safe mobility, which inevitably requires sensors to achieve autonomy, but relying too heavily on sensors will be a risk factor. A high-definition map (HD map) reduces the risk by giving geographical information if it covers dynamic information from moving entities on the road. Cooperative intelligent transport systems (C-ITS) are a prominent approach to solving the issue and local dynamic maps (LDMs) are expected to realize the ideal C-ITS. An actual LDM implementation requires a fine database design to be able to update the information to represent potential risks based on future interactions of vehicles. In the present study, we proposed an advanced method for embedding the geographical future occupancy of vehicles into the database by using a binary decision diagram (BDD). In our method, the geographical future occupancy of vehicles was formulated with Kamm’s circle. In computer experiments, sharing BDD-based occupancy data was successfully demonstrated in the ROS-based simulator with the linked list-based BDD. Algebraic operations in exchanged BDDs effectively managed future interactions such as data insertion and timing of collision avoidance in the LDM. This result opened a new door for the realization of the ideal LDM for safety in AVs.

Exploring cooperative NOMA assisted hybrid visible light and radio frequency for enhanced vehicular message dissemination at road intersections

Vehicle-to-everything (V2X) communication aims to achieve significantly improved safety and traffic efficiency, more particularly at road intersection where high percentage of accidents usually occur. The existing vehicular radio frequency (V-RF) based V2X utilizes relaying for improving safety message dissemination at road intersections. For a high traffic density scenario, the V-RF communication with relaying solution may suffer from large latency and low packet delivery rates due to channel congestion. In this paper, we explore cooperative non-orthogonal multiple access (NOMA) communication assisted hybrid vehicular visible light communication (V-VLC) and V-RF communication for improving safety message dissemination and enabling massive connectivity among vehicles for road intersection scenarios. We develop a stochastic geometry based analytical framework to model cooperative NOMA (C-NOMA) transmissions subject to interference imposed by other vehicles on roads. We also examine the impact of vehicles headlights radiation pattern viz. Lambertian and empirical path loss models on statistical characterization of the proposed C-NOMA supported hybrid solution. Our numerical findings reveal that C-NOMA assisted hybrid V-VLC/V-RF system leads to considerable improvement in outage performance and average achievable rate as compared to traditional V-RF solution with relaying. Interestingly, Lambertian model offers a lower outage and higher average achievable rate compared to the empirical model for the proposed hybrid solution. Further, we observe the performance improvement using maximal ratio combining (MRC) considering NOMA transmission for the proposed hybrid solution. The presented framework may serve as an alternative for cooperative intelligent transportation system (C-ITS) to meet diverse application needs for beyond 5G (B5G) V2X networks.

A misbehavior detection framework for cooperative intelligent transport systems

With changing times, the need for security increases in all fields, whether we talk about cloud networks or vehicular networks. In every place, it has its importance, but in vehicular networks where the lives of human beings are involved, security becomes the topmost priority. Therefore, this article aims to shed light on Misbehavior Detection Framework (MDF) used in the Cooperative Intelligent Transport Systems community. Here, MDF keeps an eye on malicious entities on the roads. It is done by regularly evaluating two main checks: consistency and local plausibility. These checks are done by Intelligent Transport System Stations. All the messages received through Vehicle-to-Everything are scrutinized through this model. After that, all the messages are evaluated by local detection mechanisms to decide the holistic message’s plausibility. This article mainly focuses on the logic behind the proposed Misbehavior Detection Framework providing more security, evaluating various Machine Learning-based models to ensure one best out of all based on quality and computation latency of all models along with the results of various parameters, such as Recall, Precision, F1 Score, Accuracy, Bookmaker Informedness, Markedness, Mathews Correlation Coefficient, Kappa, and achieved the best results.

Akıllı Ulaşım Sistemleri Mimarisi: K-AUS için Öneri

The latest advances in technology and the improvement of decision processes with learning methods based on artificial intelligence have put the word "smart" ahead of all systems that make human life easier. Based on intelligent transportation systems, it is aimed to reduce the damage to the country's economy and the environment while providing technology-based and faster, safer, more accessible, more sustainable and more efficient transportation. The main goal of creating the intelligent transportation systems architecture is to design and implement human-focused, sustainable transportation systems together with cutting-edge technologies such as industry 4.0 technologies, mobile applications, augmented reality, and the internet of things. The transition to the Cooperative Intelligent Transportation Systems (C-ITS) structure by strengthening the infrastructure of intelligent transportation systems is included in the "National Intelligent Transportation Systems Strategy Document and 2020-2023 Action Plan". Intelligent transportation systems architecture needs to be updated according to C-ITS systems that provide interoperability and data integrity. With C-ITS, the aggregate collection of intelligent systems under one roof and the integrity of data will enable sustainable mobility such as monomedical payment in multi-mode transport. Therefore, the main factor in creating the architecture of intelligent transport systems is to create system architecture by making complex systems with data integrity and numerous insignificant idle data into systems that communicate with each other and reach the level of interoperability. In this study, intelligent transportation systems policies in the world have been analyzed and systems that have reached the level of interoperability that will provide the basis of C-ITS and intelligent transportation systems architecture have been proposed.

Potential of Cooperative Intelligent Transport System Services to Mitigate Risk Factors Associated With Emergency Vehicle Accidents

Emergency vehicles (EVs) are at high risk of accidents during emergency driving. To make use of countermeasures to mitigate these risks, it is important to understand under what circumstances EV-related accidents occur. The common risk factors for EV-related accidents were examined through a systematic literature review. A total of 22 articles were examined for risk factors associated with EV-related accidents. The most reported risk factors were, in order of frequency, intersections, daytime, dry roads, clear weather, urban roads, traffic signals, and angular collisions. The articles were also reviewed for suggested countermeasures to mitigate the risk factors. The most commonly suggested countermeasures were driver training for EV operators, educating the public, exercising caution at intersections, wearing a seatbelt, and intelligent vehicle technologies. Cooperative intelligent transport systems (C-ITSs) have the potential to mitigate the risks of EV-related accidents. Therefore, three C-ITS services were investigated: EV approaching, EV preemption, and geofencing. They could all be used to inform, warn, or control aspects of driving. Each suggested service has the potential to decrease risk factors for EV-related accidents. The current literature review provides guidance on under what circumstance and in what form C-ITSs could be beneficial to prevent EV-related accidents. Further research is needed to examine behavior when drivers are introduced to C-ITSs.

A Privacy-Preserving-Based Secure Framework Using Blockchain-Enabled Deep-Learning in Cooperative Intelligent Transport System

Cooperative Intelligent Transport System (C-ITS) is a promising technology that aims to improve the traditional transport management systems. In C-ITS infrastructure Autonomous Vehicles (AVs) communicate wirelessly with other AVs, Road Side Units (RSUs) and Traffic Command Centres (TCCs) using an open channel Internet. However, the use of the Internet brings inherent vulnerabilities related to privacy (e.g., adversary performing inference and data poisoning attacks), and security (e.g., AVs can be compromised using advanced hacking techniques) issues and prevents the faster realization of C-ITS applications. To address these challenges, this paper presents a privacy-preserving-based secure framework to provide both privacy and security in C-ITS infrastructure. The proposed framework provides two level of security and privacy using blockchain and deep learning modules. First, a blockchain module is designed to securely transmit the C-ITS data between AVs–RSUs-TCCs, and a smart contract-based enhanced Proof of Work (ePoW) technique is designed to verify data integrity and mitigate data poisoning attacks. Second, a deep-learning module is designed that includes Long-Short Term Memory-AutoEncoder (LSTM-AE) technique for encoding C-ITS data into a new format to prevent inference attacks. The encoded data is used by the proposed Attention-based Recurrent Neural Network (A-RNN), for intrusive events recognition in C-ITS infrastructure. The proposed A-RNN is trained using Truncated Backpropagation Through Time (BPTT) algorithm. The framework is further validated and tested using two publicly available ToN-IoT and CICIDS-2017 datasets. The proposed framework is compared with peer privacy-preserving intrusion detection techniques, and the result shows the effectiveness of the proposed framework over several state-of-the-art techniques in both blockchain and non-blockchain systems.

Deviation Point Curriculum Learning for Trajectory Outlier Detection in Cooperative Intelligent Transport Systems

Cooperative Intelligent Transport Systems (C-ITS) are emerging in the field of transportation systems, which can be used to provide safety, sustainability, efficiency, communication and cooperation between vehicles, roadside units, and traffic command centres. With improved network structure and traffic mobility, a large amount of trajectory-based data is generated. Trajectory-based knowledge graphs help to give semantic and interconnection capabilities for intelligent transport systems. Prior works consider trajectory as the single point of deviation for the individual outliers. However, in real-world transportation systems, trajectory outliers can be seen in the groups, e.g., a group of vehicles that deviates from a single point based on the maintenance of streets in the vicinity of the intelligent transportation system. In this paper, we propose a trajectory deviation point embedding and deep clustering method for outlier detection. We first initiate network structure and nodes’ neighbours to construct a structural embedding by preserving nodes relationships. We then implement a method to learn the latent representation of deviation points in road network structures. A hierarchy multilayer graph is designed with a biased random walk to generate a set of sequences. This sequence is implemented to tune the node embeddings. After that, embedding values of the node were averaged to get the trip embedding. Finally, LSTM-based pairwise classification method is initiated to cluster the embedding with similarity-based measures. The results obtained from the experiments indicate that the proposed learning trajectory embedding captured structural identity and increased <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F-measure</i> by 5.06% and 2.4% while compared with generic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Node2Vec</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Struct2Vec</i> methods.

Distributed Offloading for Cooperative Intelligent Transportation Under Heterogeneous Networks

With the rapid advancement of the Internet of Vehicles and artificial intelligence (AI) technologies, the cooperative intelligent transportation system (C-ITS) has drawn great attention in recent years. To provide an ultra-reliable, low-latency computation experience of C-ITS, computation offloading is deemed indispensable by working with edge-cloud servers. In this paper, we first investigate a distributed dynamic computation offloading model for multi-access edge computing (MEC) enabled C-ITS under a heterogeneous road network, in which the multiple and heterogeneous computing power sources cooperatively provide computation offloading services for vehicles. Considering the autonomous offloading manner of the vehicles, we formulate the task offloading and computing power allocation as a distributed Stackelberg game, where the MEC servers as the leader to allocate computing resources and manage local energy, and the vehicles as the followers to offload local computation task. Since the observable states in the game is incomplete, the problem of resolving the optimal strategies for each game player is modeled as a partially observable Markov decision process (POMDP) to maximize the long-term cumulative reward. Then we develop a computation offloading algorithm using Stackelberg game-based multi-agent deep deterministic policy gradient (SG-MADDPG), which uses a centralized training and decentralized execution method to learn the optimal computing power allocation and computation offloading policies. Finally, extensive simulations are carried out and show the rationality and effectiveness of the proposed algorithm.

An Analytical Model of Page Dissemination for Efficient Big Data Transmission of C-ITS

With the rapid development of Cooperative Intelligent Transportation System (C-ITS), it becomes an urgent problem to effectively evaluate the data transmission efficiency of code dissemination protocols with network coding in the Dedicated Transportation Sensor Network (DTSN). First, this paper builds the overall structure of DTSN to remotely monitor the railway infrastructure in the C-ITS. Second, we propose a time model of page dissemination to reduce the deviation between the predictive data of existing models and the real-world data of code dissemination. Third, a Firefly Algorithm is designed to further improve the data transmission efficiency. The algorithm makes the feasibility rules to handle constraints, and it searches the optimal page granularity to minimize the time of page dissemination through iteration. Experiments show that the simulation results are consistent with the prediction results of proposed model, and code images can be distributed quickly and efficiently in the DTSN.

Analysis of Vehicle Location Prediction Errors for Safety Applications in Cooperative-Intelligent Transportation Systems

Cooperative-Intelligent Transportation System (C-ITS) safety applications depend on reliable location information timely exchanged by road users. Due to inter-vehicle communication delays and sampling frequency, there always exists a time gap between the state observation update time and safety decision time. Predicting the vehicle’s locations into a future time epoch common to both host and subject vehicles enables real-time collision detection. Current studies of vehicle positioning performance mostly focus on the accuracy and availability of vehicle navigation solutions at equal observation intervals. Location error propagation over the prediction time intervals and dependence on various factors is not much understood. In this paper, we analyzed how the accuracy of the location prediction degrades depending on prediction intervals and state estimate errors from the measurement updates. We adopted the Kalman Filter method to predict locations with two representative location data sets collected in real road environments. Results from a dual-frequency Global Navigation Satellite System (GNSS)/Real-time Kinematic (RTK) receiver show that the Root Mean Square Error (RMSE) of prediction locations grow from a few centimeters at the state updates to about 50 and 100 cm within the prediction intervals of 1 and 2 seconds, respectively. This implies that GNSS/RTK positioning capability is a prerequisite for C-ITS safety applications. The experimental results from a surveying-grade GNSS/Inertial Navigation System (INS) receiver show that the RMSE can remain within 10 cm for the prediction interval of 2 s. High-rate INS velocity measurements provide significant advantages in efficient control of the error growth of the predicted vehicle locations.