Learning a Lattice Planner Control Set for Autonomous Vehicles
This work addresses efficiency in motion planning for autonomous vehicles, offering an incremental improvement over prior methods.
The paper tackles the problem of generating sparse control sets for lattice planners in autonomous vehicles by learning from vehicle path datasets, resulting in up to 4.31x planning speedup while maintaining high manoeuvrability.
This paper introduces a method to compute a sparse lattice planner control set that is suited to a particular task by learning from a representative dataset of vehicle paths. To do this, we use a scoring measure similar to the Fréchet distance and propose an algorithm for evaluating a given control set according to the scoring measure. Control actions are then selected from a dense control set according to an objective function that rewards improvements in matching the dataset while also encouraging sparsity. This method is evaluated across several experiments involving real and synthetic datasets, and it is shown to generate smaller control sets when compared to the previous state-of-the-art lattice control set computation technique, with these smaller control sets maintaining a high degree of manoeuvrability in the required task. This results in a planning time speedup of up to 4.31x when using the learned control set over the state-of-the-art computed control set. In addition, we show the learned control sets are better able to capture the driving style of the dataset in terms of path curvature.