Inverse Reinforcement Learning with Dynamic Occupancy Grid Map for Urban Local Path Planning: A CNN Model Approach

In urban environments characterized by various dynamic variables such as moving vehicles, pedestrians, and bicycles, path planning for collision avoidance is essential for autonomous driving. Enhancing the stability of the route can be achieved by incorporating not only information about recognized surrounding objects but also dynamic information during the path planning process. Therefore, this study proposes a convolutional neural network-based local path planning technique for autonomous vehicles in urban environments using a dynamic occupancy grid map (DOGM). This approach ensures precision in path generation by considering the occupancy and speed of various objects. During the learning process, we implemented inverse reinforcement learning by combining trajectory information driven by expert intentions with environmental information obtained from DOGM through a combination of convolution layers. This demonstrates the feasibility of designing stable paths with low collision rates in urban areas. Particularly noteworthy is the superior performance achieved when DOGM is used as input data for deep learning, surpassing conventional algorithms.