Keyvan Golestan

Vehicle localization in VANETs using data fusion and V2V communication

In Vehicular Ad-hoc Networks (VANETs), one of the challenging issues is to find an accurate localization information. In this paper, we have addressed this problem by introducing a novel approach based on the idea of cooperative localization. Our proposed scheme incorporates different techniques of localization along with data fusion as well as vehicle-to-vehicle communication, to integrate the available data and cooperatively improve the accuracy of the localization information of the vehicles. The simulation results show that sharing the localization information and deploying that of the neighboring vehicles, not only can assure the vehicles in a vicinity to obtain more accurate localization information, but also find the results robust to sensor inaccuracies or even to failures.

Vehicular ad-hoc networks(VANETs): capabilities, challenges in information gathering and data fusion

Vehicular Ad-hoc Network (VANET) has become an active area of research due to its major role to improve vehicle and road safety, traffic efficiency, and convenience as well as comfort to both drivers and passengers. This paper thus addresses some of the attributes and challenging issues related to Vehicular Ad-hoc Networks (VANETs). A lot of VANET research work have focused on specific areas including routing, broadcasting, Quality of Service (QoS), and security. In this paper, a detailed overview of the current information gathering and data fusion capabilities and challenges in the context of VANET is presented. In addition, an overall VANET framework, an illustrative VANET scenario are provided in order to enhance safety, flow, and efficiency of the transportation system.

Situation awareness within the context of connected cars

We have conducted a literature survey on situation awareness in connected cars.Situation awareness process is categorized into four main groups.Various taxonomies are proposed for different situation awareness technical tools.The capabilities of different situation awareness tools are compared.Major situation awareness frameworks in connected cars are introduced. Driving safety is among the most important factors in the design of next generation vehicles as an integral component of Intelligent Transportation Systems. Crash avoidance and reduction of potential subsequent fatalities require timely delivery of sensitive and pertinent safety information for the drivers. Hence, the driver can become aware of the current driving situation, and can consequently, take appropriate decisions to avoid potentially imminent hazards. In this paper, we propose a comprehensive survey on situation awareness within the context of connected vehicles and Internet of Cars (also called here as connected cars). We provide context for the Internet of Cars and highlight its major features. Furthermore, situation awareness in the Internet of Cars is explored through presenting an in-depth discussion on its different components. Various aspects of high and low level information fusions are described within this context. Besides, major methods/models in situation awareness are linked to the main aspects of each component, and an overall comparison between them is reported. Moreover, on-the-road safety frameworks incorporating situation awareness are highlighted. Finally, the challenging issues and the emerging trends that shall be faced by the research community are addressed.

Vehicular ad-hoc Networks(VANETs): capabilities, challenges in context-aware processing and communication gateway

Vehicular Ad-hoc Networks (VANETs) have attracted attention in the support of safe driving, intelligent navigation, and emergency and entertainment applications. VANET can be viewed as an intelligent component of the Transportation Systems as vehicles communicate with each other as well as with roadside base stations located at critical points of the road, such as intersections or construction sites. In this paper, we provide an overview of the context-aware processing and communication gateway associated with Vehicular Ad-hoc Network (VANET). The concept of context-awareness, the recent advances and various challenges involved in context-aware processing are discussed. Some arising ideas such as based on context ontology, relevancy, hybrid dissemination, service oriented routing are also presented. This paper further briefly describes the communication gateway in VANET which includes its functional view together with the standards and their detailed preliminary specifications applicable to VANET.

Localization in vehicular ad hoc networks using data fusion and V2V communication

The paper deals with challenging localization problem in vehicular ad-hoc networks.A novel approach is proposed based on the idea of cooperative localization.Our scheme integrates available data and cooperatively improves location accuracy.Localization is more accurate and robust to sensor inaccuracies or even to failures.The estimation of vehicle prior and sequential decentralized EKF improve further. In Vehicular ad-hoc networks (VANETs), one of the challenging tasks is to find an accurate localization information. In this paper, we have addressed this problem by introducing a novel approach based on the idea of cooperative localization. Our proposed scheme incorporates different techniques of localization along with data fusion as well as vehicle-to-vehicle communication, to integrate the available data and cooperatively improve the accuracy of the localization information of the vehicles. The simulation results show that sharing the localization information and deploying that of the neighboring vehicles, not only assures the vehicles in a vicinity to obtain more accurate localization information, but also find the results robust to sensor inaccuracies or even to failures. Moreover, further improvement has been achieved by estimating the vehicle prior (prior mean and covariance) using unscented transform (UT) together with sequential decentralized extended Kalman filtering.

Recent Advances on Context-Awareness and Data/Information Fusion in ITS

Intelligent transportation systems (ITS) involve various emerging technologies and applications. This paper presents a comprehensive review of recent advances on data/information fusion and context-awareness referring to ITS. Data/Information fusion is necessary to fuse the data from different sensors and thereby extract relevant information on the target sources. On the other hand, context-aware information processing provides awareness of the driving environments by deploying intelligent query processing and smart information dissemination. The fusion and context-awareness should help in improving ITS operations with better road-awareness service, traffic monitoring, vehicle detection as well as development of new methods. This paper is centered on data fusion and context aware methodologies developed recently in the areas of ITS rather than on their ITS applications. We found that the recent progresses in ITS fusion are devoted to the potential cooperative approaches providing real-time/dynamic vehicle sensing technologies, whereas the recent context awareness techniques are deploying service concepts (e.g. location aware service) and frameworks. It is believed that the newly developed advanced fusion/context-aware techniques are becoming more effective to tackle complex traffic scenarios (e.g. traffic intersection) as well as complex urban environments.

Attention Assist: A High-Level Information Fusion Framework for Situation and Threat Assessment in Vehicular Ad Hoc Networks

Driver inattentiveness constitutes the main cause of road accidents, which makes it a major factor in road safety. In this paper, we propose a comprehensive framework to address the road safety problem by tackling it from a high-level information fusion standpoint, considering vehicular ad hoc networks (VANETs) as the deployment platform. The proposed framework relies on the multientity Bayesian networks (MEBNs), which exploit the expressiveness of first-order logic for semantic relations, and the strength of the Bayesian networks in handling uncertainty. First, the entities that influence the inattention phenomenon, as well as both their causal and semantic relationships, are identified. Next, an MEBN-based high-level information fusion framework is proposed through which entities, situations, and their relationships in specific contexts are modeled using MEBN fragments. Furthermore, MEBN inference is used to assess the situations of interest by estimating their states. To demonstrate the capabilities of the proposed framework, a collision warning system simulator has been developed, which evaluates the likelihood of a vehicle being in a near-collision situation using a wide variety of local and global information sources available in various VANET environments. If the threat of being in a near-collision situation is determined to be high, then the driver is warned accordingly. Our experimental results for two distinct single-vehicle and multivehicle categories of driving scenarios, as well as a novel hybrid MEBN inference, demonstrate the capability of the proposed framework to efficiently achieve situation and threat assessment on the road.

A model for situation and threat/impact assessment in vehicular ad-hoc networks

In this paper, a model is proposed to define situations structures and situation evolution. This model is the fundamental basis of a Threat/Impact assessment system that is implemented using a Fuzzy extension of Multi-Entity Bayesian Network in Vehicular Ad-hoc Networks. The proposed model is built on top of our previously presented situation assessment system, and completes our novel High-Level Information Fusion framework for VANET. To show the capabilities of the proposed model, a Collision Warning System in VANET is implemented in OpenDS simulation environment coupled with a real driving simulator. Furthermore, different situation structures along with situation evolution towards temporal and lateral dimensions are discussed. Finally a threat assessment system using Fuzzy MEBN is constructed on top of the situations of interest to help in identifying the source of threat.

Fuzzy multi entity Bayesian networks: A model for imprecise knowledge representation and reasoning in high-level information fusion

This paper presents a novel comprehensive Fuzzy extension to Multi-Entity Bayesian Networks (MEBN) that is deemed a well-studied and theoretically rich language that expressively handles semantics analysis, and effectively model uncertainty management. However, MEBN lack the capability of modeling the inherent conceptual and structural ambiguity that is delivered with the knowledge gained through human language. In this paper, Fuzzy MEBN that is a new version of MEBN which is based on First-order Fuzzy Logic, and Fuzzy Bayesian Networks is introduced. Furthermore, its applicability is evaluated by implementing an application related to Vehicular Ad-hoc Networks area. The results demonstrate that Fuzzy MEBN is capable of dealing with ambiguous semantical and uncertain causal relationships between the knowledge entities very efficiently.

An integrated approach for Fuzzy Multi-entity Bayesian Networks and semantic analysis for soft and hard data fusion

In this paper, a soft+hard data fusion model is proposed that is capable of combining the data generated from human-based sources with those generated by physical sensors. The basis of this model is our previously introduced Fuzzy extension to the Mutli-Entity Bayesian Network (MEBN) language, which is a High-Level Information Fusion (HLIF) framework capable of expressing the semantic and causal relationships between the entities constituting a world model, as well as managing their ambiguity and uncertainty. In our proposed model, the unstructured soft data is presented by undergoing a novel soft-data-association process, through which the data is semantically analyzed, and accurately structured in a fuzzy random variable. Moreover, the clique tree inference algorithm for Bayesian Networks is modified to handle fuzzy evidence in Fuzzy-MEBN. The simulation results, in transportation domain, show that our improved HLIF model is capable of handling both soft and hard data, and consequently, provide the user with more precise situation assessment.