Path Planning for Autonomous Vehicles with Dynamic Lane Mapping and Obstacle Avoidance

Ahmed El Mahdawy, Amr El Mougy

2021

Abstract

Path planning is at the core of autonomous driving capabilities, and obstacle avoidance is a fundamental part of autonomous vehicles as it has a great effect on passenger safety. One of the challenges of path planning is building an accurate map that responds to changes in the drivable area. In this paper, we present a novel path planning model with static and moving obstacle avoidance capabilities, LiDAR-based localization, and dynamic lane mapping according to road width. We describe our cost-based map building approach and show the vehicle trajectory model. Then, we evaluate our model by performing a simulation test as well as a real life demo, in which the proposed model proves to be effective at maneuvering around static road obstacles, as well as avoiding collisions with moving obstacles such as in pedestrian crossing scenarios.

Paper Citation

in Harvard Style

El Mahdawy A. and El Mougy A. (2021). Path Planning for Autonomous Vehicles with Dynamic Lane Mapping and Obstacle Avoidance.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-484-8, pages 431-438. DOI: 10.5220/0010342704310438

in Bibtex Style

@conference{icaart21,
author={Ahmed El Mahdawy and Amr El Mougy},
title={Path Planning for Autonomous Vehicles with Dynamic Lane Mapping and Obstacle Avoidance},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2021},
pages={431-438},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010342704310438},
isbn={978-989-758-484-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Path Planning for Autonomous Vehicles with Dynamic Lane Mapping and Obstacle Avoidance
SN - 978-989-758-484-8
AU - El Mahdawy A.
AU - El Mougy A.
PY - 2021
SP - 431
EP - 438
DO - 10.5220/0010342704310438