# Decoupled Sampling Based Planning Method for Multiple Autonomous Vehicles

## TL;DR

This paper introduces a novel sampling-based planning algorithm for multiple autonomous vehicles, enhancing RRT with a two-stage sampling strategy and K-nearest points to improve maneuverability and collision avoidance.

## Contribution

It presents an improved RRT algorithm with a two-stage sampling strategy and K-nearest points for better maneuvering in multi-vehicle autonomous driving.

## Key findings

- Successful collision avoidance in simulations
- Enhanced maneuverability of autonomous vehicles
- Effective planning in complex scenarios

## Abstract

This paper proposes a sampling based planning algorithm to control autonomous vehicles. We propose an improved Rapidly-exploring Random Tree which includes the definition of K- nearest points and propose a two-stage sampling strategy to adjust RRT in other to perform maneuver while avoiding collision. The simulation results show the success of the algorithm.