Improve Bounding Box in Carla Simulator

Mohamad Chaar, Jamal Raiyn, Galia Weidl

2024

Abstract

The CARLA simulator (Car Learning to Act) serves as a robust platform for testing algorithms and generating datasets in the field of Autonomous Driving (AD). It provides control over various environmental parameters, enabling thorough evaluation. Development bounding boxes are commonly utilized tools in deep learning and play a crucial role in AD applications. The predominant method for data generation in the CARLA Simulator involves identifying and delineating objects of interest, such as vehicles, using bounding boxes. The operation in CARLA entails capturing the coordinates of all objects on the map, which are subsequently aligned with the sensor’s coordinate system at the ego vehicle and then enclosed within bounding boxes relative to the ego vehicle’s perspective. However, this primary approach encounters challenges associated with object detection and bounding box annotation, such as ghost boxes. Although these procedures are generally effective at detecting vehicles and other objects within their direct line of sight, they may also produce false positives by identifying objects that are obscured by obstructions. We have enhanced the primary approach with the objective of filtering out unwanted boxes. Performance analysis indicates that the improved approach has achieved high accuracy.

Paper Citation

in Harvard Style

Chaar M., Raiyn J. and Weidl G. (2024). Improve Bounding Box in Carla Simulator. In Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-703-0, SciTePress, pages 267-275. DOI: 10.5220/0012600500003702

in Bibtex Style

@conference{vehits24,
author={Mohamad Chaar and Jamal Raiyn and Galia Weidl},
title={Improve Bounding Box in Carla Simulator},
booktitle={Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2024},
pages={267-275},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012600500003702},
isbn={978-989-758-703-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - Improve Bounding Box in Carla Simulator
SN - 978-989-758-703-0
AU - Chaar M.
AU - Raiyn J.
AU - Weidl G.
PY - 2024
SP - 267
EP - 275
DO - 10.5220/0012600500003702
PB - SciTePress