用于卡车-半挂车组合铰接角估计的混合机器学习
Hybrid Machine Learning for Articulation Angle Estimation of Truck-Semitrailer Combinations
浏览论文内容
中文总结 AI 辅助
研究针对卡车-半挂车组合铰接角估计问题，提出基于学习的模型并集成于EKF框架，应用自适应加权方案，通过大量实际实验验证，该混合方法能准确可靠估计铰接角，降低实施要求，具有实际部署优势。
中文摘要 AI 辅助
对于自动驾驶和高级驾驶员辅助系统（ADAS）而言，准确估计带挂车卡车的铰接角至关重要。现有方法存在诸多局限，如需要手动初始化、额外传感器、先验知识或挂车信号，且缺乏实际验证。本文提出多种基于学习的模型，直接从视觉和运动学输入估计铰接角，无需专门驾驶操作初始化等。将两个基于学习的模型与运动学模型集成于扩展卡尔曼滤波器（EKF）框架，对视觉输入测量应用基于不确定性量化的自适应加权方案。大量不同挂车类型的实际实验证明了该方法在域外条件下的鲁棒性和泛化能力，结果表明混合方法能实现准确可靠的铰接角估计，同时降低实施要求并具备实际部署优势。
英文摘要
Accurate articulation angle estimation of trucks with trailers is critical for autonomous driving and advanced driver assistance system (ADAS). Existing methods either require manual initialization, additional sensors, or prior knowledge and signals from trailers, or they lack real-world validation, limiting practical deployment. This paper presents multiple learning-based models to directly estimate articulation angles from visual and kinematic inputs, eliminating the need for dedicated driving maneuvers for initialization, bounding box annotations, trailer-mounted sensor signals, or prior knowledge of trailer parameters. Two learning-based models are integrated with a kinematic model within an extended Kalman filter (EKF) framework, and an adaptive weighting scheme based on uncertainty quantification is applied for measurements involving visual input. Extensive real-world experiments with different trailer types demonstrate the approaches' robustness and generalization under out-of-domain conditions, including new trailers, varying colors, and lighting conditions. Results show that the hybrid method achieves accurate and reliable articulation angle estimation while maintaining reduced implementation requirements and practical deployment advantages.
发表机构
- ZF CV Systems Hannover GmbH（采埃孚商用车系统汉诺威有限公司）
- Leibniz University Hannover（汉诺威莱布尼茨大学）
机构由 AI 辅助整理，请以论文原文为准。
