Hybrid Machine Learning for Articulation Angle Estimation of Truck-Semitrailer Combinations

2026-07-18

AI Summary

Accurately estimating the articulation angle of tractor-semitrailer combinations is critical for autonomous driving, yet existing approaches often rely on manual initialization, additional sensors, or trailer-side signals, hindering practical deployment. This work proposes a hybrid learning model that fuses visual and kinematic inputs to directly estimate the articulation angle and integrates it into an extended Kalman filter framework. By employing an uncertainty-aware adaptive weighting strategy, the method effectively fuses multimodal information without requiring prior knowledge of trailer parameters, specialized initialization, trailer-mounted sensors, or annotated data. Evaluated across diverse real-world trailer types, colors, and lighting conditions, the approach demonstrates high accuracy, strong robustness, and excellent out-of-domain generalization, substantially lowering the barrier to real-world deployment.

Abstract

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.

Problem

• articulation angle estimation
• truck-semitrailer
• autonomous driving
• ADAS
• real-world validation

Innovation

• hybrid machine learning
• articulation angle estimation
• extended Kalman filter
• uncertainty quantification
• vision-based kinematic fusion