Keyvan Golestan Irani
Information Fusion Methodology for Enhancing Situation Awareness in Connected Cars Environment
In this seminar, novel approaches to develop a comprehensive framework to address situation awareness in the Internet of Cars, called Attention Assist Framework (AAF) are introduced. The proposed model utilizes both Low-Level Data Fusion (LLDF), and High-Level Information Fusion (HLIF) to implement traffic entity, situation, and impact assessment, as well as decision making.
The Internet of Cars is the convergence of the Internet of Things and Vehicular Ad-hoc Networks (VANETs). In fact, VANETs are the communication platforms that make possible the implementation of the Internet of Cars, and has become an integral part of this research field due to its major role to improve vehicle and road safety, traffic efficiency, and convenience as well as comfort to both drivers and passengers. Significant amount of VANETs research work has been focused on specific areas such as safety, routing, broadcasting, Quality of Service (QoS), and security. Among them, road safety issues are deemed one of the most challenging problems of VANETs. Specifically, lack of proper situational awareness of drivers has been shown to be the main cause of road accidents which makes it a major factor in road safety.
The traffic entity assessment relies on a LLDF framework that is able to incorporate various multi-sensor data fusion approaches with means of communication links in VANETs. This is used to implement a cooperative localization approach through fusing common data fusion methods, such as Extended Kalman Filter (EKF) and Unscented Transform (UT), and vehicle-to-vehicle communication in VANETs. Furthermore, traffic situation assessment is based on a fuzzy extension to the Multi-Entity Bayesian Networks (MEBNs), which exploit the expressiveness of first-order logic for semantic relations, and the strength of the Fuzzy Bayesian Networks in handling uncertainty, while tackling the inherent vagueness in the soft data created by human entities. Finally, the impact assessment and decision making is realized through incorporating notions of game theory into Fuzzy-MEBNs, and introducing Active Fuzzy-MEBN (ATFY-MEBN), which is capable in hypothesizing future situations by assessing the impact of the current situation upon taking the actions indicated by an optimal strategy. In fact, such strategies are achieved through solving the games that are generated through a novel situation-specific normal form games generation algorithm that aims to create games based on the given context. In general, ATFY-MEBN presents the concepts of players and actions, and includes new game components, along with a 2-tier architecture, to efficiently model impact assessment and decision making.
To demonstrate the capabilities of the proposed framework, a collision warning system simulator is developed, which evaluates the likelihood of a vehicle being in a near-collision situation using a wide variety of both local and global information sources available in the VANETs environment, and suggests an optimal action by assessing the impact of the current situation through generating and solving situation-specific games.