Autonomous driving requires tactical decision-making while navigating in a dynamic shared space environment. The complexity and uncertainty in this process arise due to unknown and tightly-coupled interaction among traffic users. This thesis work formulates an unknown navigation problem as a Markov decision process (MDP), supported by models of traffic participants and userspace. Instead of modeling a traditional MDP, this work formulates a Multi-policy decision making (MPDM) in a shared space scenario with pedestrians and vehicles. The employed model enables a unified and robust self-driving of the ego vehicle by selecting a desired policy along the pre-planned path. Obstacle avoidance is coupled within the navigation module performing a detour off the planned path and obtaining a reward on task completion and penalizing for collision with others. In addition to this, the thesis work is further extended by analyzing the real-time constraints of the proposed model. The performance of the implemented framework is evaluated in a simulation environment on a typical construction (quarry) scenario. The effectiveness and efficiency of the elected policy verify the desired behavior of the autonomous vehicle.