Before revision:  Our solution, called the modification architecture, uses a path following algorithm to follow a reference path and a neural network to modify the references generated by the path follower in order to prevent collisions. The neural network is trained using reinforcement learning to deviate from the reference trajectory just enough to avoid crashing. We present our local planner in a hierarchical planning framework, nested between a global reference planner and a low-level control system. The system is evaluated in the context of F1/10th scale autonomous racing in random forests and on a race track .
After revision:  Our solution, called the modification architecture, uses a path following algorithm to follow a reference path and a neural network to modify the references generated by the path follower to avoid obstacles. Importantly, our architecture does not require an updated obstacle map and only 10 laser range finders to avoid obstacles. The modification planner is evaluated in the context of F1/10th scale autonomous racing in random forests and on a race track .