On-road autonomous vehicle navigation requires real-time motion planning in the presence of static and moving objects. Based on sensed data of the environment and the current traffic situation, an autonomous vehicle has to plan a path by predicting the future location of objects of interest. In this context, an object of interest is a moving or stationary object in the environment that has a reasonable probability of intersecting the path of the autonomous vehicle within a predetermined time frame. This paper investigates the identification of objects of interest within the PRIDE (PRediction In Dynamic Environments) framework. PRIDE is a multi-resolutional, hierarchical framework that predicts the future location of moving objects for the purposes of path planning and collision avoidance for an autonomous vehicle. Identifying objects of interest is an aspect of situation awareness and is performed in PRIDE using a dangerous zone, i.e., a fuzzy-logic-based approach representing a hazardous space area around an autonomous vehicle. Once objects of interest are identified, the risk of collision between the autonomous vehicle and each object of interest is then evaluated. To illustrate the performance of a dangerous zone within PRIDE, preliminary results are presented using a traffic scenario with the high-fidelity physics-based framework for the Unified System for Automation and Robot Simulation (USARSim).