提出5种轻量级车道保持算法，实测每帧处理<10毫秒，适合低算力车辆。
Evaluating Low-Resource Lane Following Algorithms for Compute-Constrained Automated Vehicles
- 采用无监督学习分离车道线，每帧处理时间低于10毫秒。
- 在不同光照、路面和弯道下表现稳定，优于高算力深度学习模型。
- 适合部署于算力受限的智能驾驶系统，提升主动安全普及性。
可靠的车道保持对自动驾驶和辅助驾驶至关重要，但现有方案多依赖高算力模型，限制了在算力受限车辆中的部署。本文评估了五种专为低算力环境设计的实时车道保持算法。通过仿真与真实驱动-线电车部署验证，评估指标包括可靠性、舒适性、速度及适应性。表现最优的方法采用无监督学习检测并分离车道线，单帧处理时间低于10毫秒，优于计算密集型且泛化能力差的深度学习方法。这些方法在不同光照条件、道路纹理和车道几何下均表现出强鲁棒性。研究结果表明，高效车道检测技术可显著提升自动驾驶技术的可访问性与可靠性。降低算力需求使车道保持功能可在低级别自动化车辆中广泛部署，如主动安全系统。
原文摘要 · Abstract (English)
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.
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