Hybrid Machine Learning for Articulation Angle Estimation of Truck-Semitrailer Combinations
For autonomous driving and ADAS in trucking, this work provides a practical, sensor-light solution for articulation angle estimation that generalizes to new trailers and conditions without requiring trailer-specific calibration.
This paper proposes hybrid machine learning models integrated with a kinematic model via an extended Kalman filter to estimate articulation angles of truck-semitrailer combinations from visual and kinematic inputs, eliminating the need for manual initialization, additional sensors, or trailer-specific prior knowledge. Real-world experiments demonstrate robustness and generalization across different trailer types and conditions, achieving accurate estimation with reduced implementation requirements.
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.