About a decade ago the idea of cooperation has been introduced to self-driving with the aim to enhance safety in dangerous places such as intersections. Infrastructure-based cooperative systems emerged very recently bringing a new point of view of the scene and more computation power. In this paper, we want to go beyond the framework presented in the vehicle-to-infrastructure (V2I) cooperation by including the vehicle's point of view in the perception of the environment. To keep the cost low, we decided to use only two-dimensional bounding boxes, thus depriving ourselves of depth information that contrasts with state-of-the-art methods. With this in-thescene point-of-view, we propose a new framework to generate a cooperative evidential occupancy grid based on the Dempster-Shafer Theory and which employs a Monte Carlo framework to incorporate position noise in our algorithm. We also provide a new cooperative dataset generator based on the CARLA simulator. Finally, we provide an extended review of our new cooperative occupancy grid map generation method which improves the state-of-the-art techniques.