Deep learning based approaches to enhance energy efficiency in autonomous driving systems

Juhyeon Kwak, Yongryeong Lee, Minje Choi and Seungjae Lee

Energy, 2024, vol. 307, issue C

Abstract: Energy efficiency is a crucially raised issue in the transportation sector. Moreover, recent technological advancements have brought attention to autonomous driving systems, which can potentially reduce energy wastage associated with human factors. This study introduces an approach to evaluate deep learning models by considering energy efficiency alongside accuracy, incorporating computation complexity as a key metric. Proposing a flexible method for choosing object detection algorithms based on driving environments, the study highlights the trade-off between accuracy and energy efficiency. The dynamic selection of algorithms is shown to optimize energy consumption in autonomous vehicles, especially in varying driving conditions. Furthermore, the study recommends an Explainable Artificial Intelligence (XAI)-based approach to find a compromise in model accuracy, enhancing transparency and societal acceptance of autonomous systems. Future research can focus on extending the data collection process to a wider range of different environmental conditions, which allows for a more robust comparison of the models and a deeper understanding of the trade-offs.

Keywords: Autonomous driving; Energy efficiency; Object detection; Deep learning; explainable AI (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224023995
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:307:y:2024:i:c:s0360544224023995

DOI: 10.1016/j.energy.2024.132625

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().