Information fusion methodology for enhancing situation awareness in connected cars environment

This dissertation introduces novel approaches to develop a comprehensive model to address situation awareness in the Internet of Cars, called Attention Assist Framework (AAF). The proposed framework 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. 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. Our experimental results are divided into three main sections, through which we evaluate the capabilities of the traffic entity, situation, and impact assessment methods. Accordingly, the performance of the proposed cooperative localization approach is assessed by comparing its results with the ground truth solution and that of the other localization methods in various driving test cases. Moreover, two distinct single-vehicle and multi-vehicles categories of driving scenarios, as well as a novel hybrid MEBN inference, demonstrate the capabilities of the proposed traffic assessment model to efficiently achieve situation and threat assessment on the road. Finally, the impact assessment and decision making models are evaluated through two different scenarios of driving in highway and intersection that are formed with various number of player vehicles, and their actions.

• Record URL:
  • http://hdl.handle.net/10012/10168
• Supplemental Notes:
  • PhD thesis
• Authors:
  • Golestan, K
• Publication Date: 2016-1

Language

• English

Media Info

• Pagination: 1 file

Subject/Index Terms

• TRT Terms: Advanced traffic management systems; Advanced traffic management systems; Behavior; Decision making; Drivers; Mathematical models; Vehicle to infrastructure communications; Vehicle to vehicle communications
• ATRI Terms: Advanced traffic management systems (ATMS); Decision process; Driver behaviour; Modelling; Vehicle to roadside communications; Vehicle to vehicle communications
• ITRD Terms: 8735: Intelligent transport system
• Subject Areas: Operations and Traffic Management;

Filing Info

• Accession Number: 01599072
• Record Type: Publication
• Source Agency: ARRB
• Files: ATRI
• Created Date: May 18 2016 9:27AM