# Search-Based Motion Planning for Performance Autonomous Driving

## TL;DR

This paper presents a search-based motion planning method for autonomous driving that optimizes vehicle trajectories to minimize lap time on slippery roads, explicitly considering nonlinear vehicle dynamics and constraints.

## Contribution

It introduces a novel search-based planning approach that explicitly models nonlinear vehicle dynamics and constraints for safe, optimal performance in challenging driving scenarios.

## Key findings

- Effective in simulated track with varying curvature
- Achieves minimum lap time on slippery roads
- Handles nonlinear vehicle dynamics explicitly

## Abstract

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.