# Search-Based Motion Planning for Performance Autonomous Driving

arXiv: https://arxiv.org/abs/1907.07825 · HTML: https://mlsanity.com/paper/1907.07825/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2019.

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

- [Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles](https://mlsanity.com/paper/2301.10384/) (2023)
- [A Multi-Heuristic Search-based Motion Planning for Automated Parking](https://mlsanity.com/paper/2307.07857/) (2023)
- [Vision for Bosnia and Herzegovina in Artificial Intelligence Age: Global Trends, Potential Opportunities, Selected Use-cases and Realistic Goals](https://mlsanity.com/paper/2209.03990/) (2022)

## Abstract (arXiv)

Driving on the limits of vehicle dynamics requires predictive planning of future vehicle states. In this work, a search-based motion planning is used to generate suitable reference trajectories of dynamic vehicle states with the goal to achieve the minimum lap time on slippery roads. The search-based approach enables to explicitly consider a nonlinear vehicle dynamics model as well as constraints on states and inputs so that even challenging scenarios can be achieved in a safe and optimal way. The algorithm performance is evaluated in simulated driving on a track with segments of different curvatures.