Operation in a real world traffic requires the ability to plan motion in complex environments (multiple moving participants) from autonomous vehicles. Navigation through such environments necessitates the provision of the right search space for the trajectory or maneuver planners so that the safest motion for the ego vehicle can be identified. Analyzing risks based on the predicted trajectories of all traffic participants (given the current state of the environment and its participants) aids in the proper formulation of this search space. This study introduces a fresh taxonomy of safety and risk that an autonomous vehicle should be capable of handling. It formulates a reference system architecture for implementation as well as describes a novel way of identifying and predicting the behaviors of other traffic participants utilizing classic Multi Model Adaptive Estimation (MMAE). Detailed simulation results and a discussion about the associated tuning of the implemented model conclude this work.