# Learning a Lattice Planner Control Set for Autonomous Vehicles

arXiv: https://arxiv.org/abs/1903.02044 · HTML: https://mlsanity.com/paper/1903.02044/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization](https://mlsanity.com/paper/1904.08784/) (2019)
- [A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems](https://mlsanity.com/paper/2509.22379/) (2025)
- [MeshA*: Efficient Path Planning With Motion Primitives](https://mlsanity.com/paper/2412.10320/) (2024)

## Abstract (arXiv)

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 Fr'echet 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.