无需初始化或额外传感器，用视觉和运动数据估算卡车挂车转角。
Hybrid Machine Learning for Articulation Angle Estimation of Truck-Semitrailer Combinations
- 融合视觉与运动数据的混合学习模型，直接估计转角。
- 实测在多种新拖车、光照下保持高精度，误差可控。
- 适合自动驾驶和辅助驾驶系统，部署门槛低。
准确估计带挂车的卡车转角对自动驾驶和高级驾驶辅助系统至关重要。现有方法通常需人工初始化、额外传感器、拖车信号或先验参数，或缺乏真实世界验证，限制实际应用。本文提出多个基于学习的模型，直接从视觉和运动输入估计转角，无需专用驾驶操作、边界框标注、拖车传感器信号或拖车参数先验。两个学习模型与运动模型结合，在扩展卡尔曼滤波框架中运行，并采用基于不确定性量化的自适应加权策略处理视觉输入。针对不同拖车型号的大量真实场景实验表明，该方法在跨域条件下（如新拖车、颜色变化、光照差异）具有强鲁棒性和泛化能力。结果表明，混合方法在保持低实现成本的同时，实现了高精度且可靠的转角估计，具备显著实用部署优势。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。