In this paper, we study the multi-hop on-demand ridesharing between riders and drivers which are represented as autonomous and rational agents. The goal is to reach the best balance between ridesharing supply and demand. In this context, each agent has its own dynamic perception represented by a bounding box and computed according to its respective constraints and preferences. These perceptions are stored in a spatial R-Tree index allowing users to perform perception overlap queries and identify possible trip shares. The evaluation and selection of optimal path shares is performed by the rider agent based on its objective function. We perform experiments by varying the detour factor of the drivers and demonstrate the validity of our model. We point out the need for optimization on the selection of the optimal transfer node. Finally, we prove the efficiency of our multi-agent based multi-hop ridesharing in terms of service rate and saved distance.