Hybrid Machine Learning for Articulation Angle Estimation of Truck-Semitrailer Combinations
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 wi...
| Authors | Qixuan Zhang, Jonas Boettcher, Simon F. G. Ehlers et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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