Evaluating Low-Resource Lane Following Algorithms for Compute-Constrained Automated Vehicles
This work addresses the deployment of lane-following in compute-constrained vehicles, which is incremental as it evaluates existing low-resource algorithms rather than proposing a new method.
The paper tackled the problem of lane-following for automated vehicles under compute constraints by evaluating five low-resource algorithms, finding that top methods using unsupervised learning achieved processing times under 10 ms per frame and outperformed compute-intensive deep learning approaches.
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.