Occupancy grid maps are fundamental to autonomous driving algorithms, offering insights into obstacle distribution and free space within an environment. These maps are used for safe navigation and decision-making in self-driving applications, forming a crucial component of the automotive perception framework. An occupancy map is a discretized representation of a chosen environment that is constructed using point cloud information obtained from sensor modalities like LiDAR and radar. In this project, we formulate the problem of estimating the occupancy grid map using sensor point cloud data as a sparse binary occupancy value reconstruction problem. We utilize the inherent sparsity of occupancy grid maps commonly encountered in automotive scenarios. Besides, the spatial dependencies between the grid cells are exploited to provide a better reconstruction of the boundaries of the objects inside the range of the map and to suppress the false alarms emerging from the reflections coming from the road. To address sparsity and spatial correlation jointly, we propose an occupancy grid estimation method that is based on pattern-coupled sparse Bayesian learning. The proposed method shows enhanced detection capabilities compared to two benchmark methods, based on qualitative and quantitative performance evaluation with scenes from the automotive datasets nuScenes and RADIal.