A processor-implemented method for a hierarchical network based diverse trajectory proposal, the method comprising: receiving an input sensor data and a trajectory mask, by one or more hardware processors, wherein the input sensor data includes a plurality of sensor data received from a plurality of sensors, wherein the trajectory mask is a set of grid cells occupied during a navigation track history of a plurality of autonomous navigation systems; pre-processing the input sensor data, by one or more hardware processors, to obtain an occupancy map based on a mapping technique; generating, by the one or more hardware processors, a ground truth trajectory using the occupancy map and the trajectory mask based on a rapidly exploring random tree star technique;. predicting, by the one or more hardware processors, a set of diverse traversable regions for an autonomous navigation system using a trajectory proposal network (TPNet), wherein the TPNet is a Convolutional Neural Network trained using a multiple choice learning technique based on the ground truth trajectories, where the multiple choice learning technique includes a set of loss functions; identifying, by the one or more hardware processors, a goal point for each of the set of diverse traversable regions based on a goal identification technique, wherein the goal point is a final coordinate position in a current map that must be reached by the autonomous navigation system, wherein the current map is a real time map received by the autonomous navigation system and the current map includes the final co-ordinate position that must be reached by the autonomous navigation system and the goal identification technique identifies a highest probability co-ordinate position in a diverse traversable region that is greater than a pre-defined threshold and farthest from the autonomous navigation system's current co-ordinate position; generating, by the one or more hardware processors, a set of ground truth waypoints for each of the set of diverse traversable regions between the goal point and a current co-ordinate position of the autonomous navigation system in the current map based on an RRT star technique; predicting, by the one or more hardware processors, a set of trajectory waypoints for each of the set of diverse traversable regions to obtain a set of diverse trajectories using a trajectory sampler network ( TSNet), wherein the TSNet is a Convolutional Neural Network - a Long Short Term Memory (LSTM) network trained using the set of diverse traversable regions and the set of ground truth waypoints and the set of trajectory waypoints is a set of co-ordinate positions along the set of diverse trajectories; and displaying the set of diverse trajectories along with its associated set of trajectory waypoints. receiving an input sensor data and a trajectory mask, by one or more hardware processors, wherein the input sensor data includes a plurality of sensor data received from a plurality of sensors, wherein the trajectory mask is a set of grid cells occupied during a navigation track history of a plurality of autonomous navigation systems; pre-processing the input sensor data, by one or more hardware processors, to obtain an occupancy map based on a mapping technique; generating, by the one or more hardware processors, a ground truth trajectory using the occupancy map and the trajectory mask based on a rapidly exploring random tree star technique;. predicting, by the one or more hardware processors, a set of diverse traversable regions for an autonomous navigation system using a trajectory proposal network (TPNet), wherein the TPNet is a Convolutional Neural Network trained using a multiple choice learning technique based on the ground truth trajectories, where the multiple choice learning technique includes a set of loss functions; identifying, by the one or more hardware processors, a goal point for each of the set of diverse traversable regions based on a goal identification technique, wherein the goal point is a final coordinate position in a current map that must be reached by the autonomous navigation system, wherein the current map is a real time map received by the autonomous navigation system and the current map includes the final co-ordinate position that must be reached by the autonomous navigation system and the goal identification technique identifies a highest probability co-ordinate position in a diverse traversable region that is greater than a pre-defined threshold and farthest from the autonomous navigation system's current co-ordinate position; generating, by the one or more hardware processors, a set of ground truth waypoints for each of the set of diverse traversable regions between the goal point and a current co-ordinate position of the autonomous navigation system in the current map based on an RRT star technique; predicting, by the one or more hardware processors, a set of trajectory waypoints for each of the set of diverse traversable regions to obtain a set of diverse trajectories using a trajectory sampler network ( TSNet), wherein the TSNet is a Convolutional Neural Network - a Long Short Term Memory (LSTM) network trained using the set of diverse traversable regions and the set of ground truth waypoints and the set of trajectory waypoints is a set of co-ordinate positions along the set of diverse trajectories; and displaying the set of diverse trajectories along with its associated set of trajectory waypoints.