Evaluating Low-Resource Lane Following Algorithms for Compute-Constrained Automated Vehicles

TL;DR

This paper evaluates five low-resource, real-time lane-following algorithms for automated vehicles, demonstrating their robustness and efficiency in simulation and real-world tests, highlighting their potential for widespread deployment in compute-constrained vehicles.

Contribution

It introduces and assesses five novel low-resource lane-following algorithms optimized for real-time operation on limited hardware, outperforming traditional deep learning methods.

Findings

Unsupervised learning methods achieved processing under 10 ms per frame.

Robust performance across various lighting and road conditions.

Potential for widespread deployment in low-resource automated vehicle systems.

Abstract

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.