Context Modeling for Safety-Critical V2V Communication
PhD. Comprehensive Exam by: Wardah Saleh
Date: Sept 8, 2026
Time: 11 am
Abstract: Vehicle-to-Vehicle (V2V) communication enables cooperative safety applications through periodic exchange of messages such as the Basic Safety Message (BSM) and Cooperative Awareness Message (CAM). Although these messages provide critical vehicle-state information, their interpretation depends on surrounding traffic, road, environmental, and driving conditions. The same message values may therefore represent safe or unsafe behaviour in different contexts. This work investigates context modeling for safety-critical V2V communication. It examines the distinction between raw data, contextual indicators, and situational awareness; analyzes rule-based, probabilistic, learning-based, semantic, and hybrid modeling approaches; and categorizes relevant context sources across message, mobility, network, environmental, and situational domains. The work identifies key challenges, including fragmented context representation, limited validation of contextual interpretations, and insufficient treatment of uncertainty and temporal dynamics. It also considers future directions such as decision-ready context architectures, threat-aware reasoning, and benchmark frameworks for evaluating context models. Overall, our work highlights the need for structured, interpretable, and reliable context modeling to support safety decision-making in cooperative and autonomous transportation systems.
PhD Committee:
Internal Reader: Dr. Saeed Samet
Internal Reader: Dr. Muhammad Asaduzzaman
External Reader: Dr. Ning Zhang
(Faculty/department)
Advisor: Dr. Ikjot Saini