A Review on Smart Navigation Techniques for Automated Vehicle

Intelligent transport is now a vital facilitator of the smart city concept, with navigation serving as a crucial component. Recently, software as well as hardware research have focused a lot of emphasis on automated vehicles (AV). The AV now provides more flexible and efficient industrial and transportation system solutions. The navigation strategy used by an AV is crucial to its functionality. Even if using AV navigation seems appropriate and sufficient, making the choice is not simple. Systems based on the Automated Vehicles (AV) arise to offer car users navigation services, and they are distinguished by using Roadside Units (RSUs) to gather data on the state of the roads from surrounding vehicles. However, the design of the vehicle navigation systems must adhere to strict performance standards for autonomous operations. Relevant performance metrics take into account the system’s accuracy as well as its capacity to identify sensor problems within a given Time-to-Alert (TTA) window and without producing a false alarm. However, they solely concentrate on collecting features from the traffic patterns of isolated or nearby intersections, despite recent research using deep reinforcement learning algorithms for traffic light control showing promising outcomes. In this paper, different navigation techniques and algorithms that are used for smart navigation are explained. In order to provide readers an idea of how Reinforcement Learning may be used in an autonomous vehicle for navigation is discussed in this article incorporation of artificial intelligence (AI).