Reliable lane-following is essential for automated and assisted driving, yet existing solutions often rely on models that require extensive computational resources, limiting their deployment in compute-constrained vehicles. We evaluate five low-resource lane-following algorithms designed for real-time operation on vehicles with limited computing resources. Performance was assessed through simulation and deployment on real drive-by-wire electric vehicles, with evaluation metrics including reliability, comfort, speed, and adaptability. The top-performing methods used unsupervised learning to detect and separate lane lines with processing time under 10 ms per frame, outperforming compute-intensive and poor generalizing deep learning approaches. These approaches demonstrated robustness across lighting conditions, road textures, and lane geometries. The findings highlight the potential for efficient lane detection approaches to enhance the accessibility and reliability of autonomous vehicle technologies. Reducing computing requirements enables lane keeping to be widely deployed in vehicles as part of lower-level automation, including active safety systems.
@article{arxiv.2409.03114,
title = {Evaluating Low-Resource Lane Following Algorithms for Compute-Constrained Automated Vehicles},
author = {Beñat Froemming-Aldanondo and Tatiana Rastoskueva and Michael Evans and Marcial Machado and Anna Vadella and Rickey Johnson and Luis Escamilla and Milan Jostes and Devson Butani and Ryan Kaddis and Chan-Jin Chung and Joshua Siegel},
journal= {arXiv preprint arXiv:2409.03114},
year = {2025}
}
Comments
Supported by the National Science Foundation under Grants No. 2150292 and 2150096