Publication

Anticipatory kinodynamic motion planner for computing the best path and velocity trajectory in autonomous driving

Journal Article (2019)

Journal

Robotics and Autonomous Systems

Pages

93-105

Volume

114

Doc link

https://doi.org/10.1016/j.robot.2018.11.022

File

Download the digital copy of the doc pdf document

Abstract

This paper presents an approach, using an anticipatory kinodynamic motion planner, for obtaining the best trajectory and velocity profile for autonomous driving in dynamic complex environments, such as driving in urban scenarios. The planner discretizes the road search space and looks for the best vehicle path and velocity profile at each control period of time, assuming that the static and dynamic objects have been detected. The main contributions of the work are in the anticipatory kinodynamic motion planner, in a fast method for obtaining the G2-splines for path generation, and in a method to compute and select the best velocity profile at each candidate path that fulfills the vehicle kinodynamic constraints, taking into account the passenger comfort. The method has been developed and tested in MATLAB through a set of simulations in different representative scenarios, involving fixed obstacles and moving vehicles. The outcome of the simulations shows that the anticipatory kinodynamic planner performs correctly in diverse dynamic scenarios, maintaining smooth accelerations for passenger comfort.

Categories

automation, control theory.

Author keywords

Autonomous driving ADAS Urban Anticipation Kinodynamic motion planning Path planning G2-splines Velocity profiles

Scientific reference

J. Pérez and A. Sanfeliu. Anticipatory kinodynamic motion planner for computing the best path and velocity trajectory in autonomous driving. Robotics and Autonomous Systems, 114: 93-105, 2019.