Tracking Accuracy Evaluation of Autonomous Agricultural Tractors via Rear Three-Point Hitch Estimation Using a Hybrid Model of EKF Transformer
The objective of this study was to improve measurement accuracy in the evaluation of autonomous agricultural tractor performance by addressing external disturbances, such as sensor installation errors, vibrations, and heading-induced bias that occur during the measurement of the conventional rear th...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/14/1475 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850077198184087552 |
|---|---|
| author | Eun-Kuk Kim Tae-Ho Han Jun-Ho Lee Cheol-Woo Han Ryu-Gap Lim |
| author_facet | Eun-Kuk Kim Tae-Ho Han Jun-Ho Lee Cheol-Woo Han Ryu-Gap Lim |
| author_sort | Eun-Kuk Kim |
| collection | DOAJ |
| description | The objective of this study was to improve measurement accuracy in the evaluation of autonomous agricultural tractor performance by addressing external disturbances, such as sensor installation errors, vibrations, and heading-induced bias that occur during the measurement of the conventional rear three-point hitch (Rear 3-Point) system. To mitigate these disturbances, the measurement point was relocated to the cab, where external interference is comparatively minimal. However, in compliance with the ISO 12188 standard, the Rear 3-Point system must be used as the reference measurement point. Therefore, its coordinates were indirectly estimated using an extended Kalman filter (EKF) and artificial intelligence (AI)-based techniques. A hybrid model was developed in which a transformer-based AI model was trained using the Rear 3-Point coordinates predicted by EKF as the ground truth. While traditional time-series models, such as LSTM and GRU, show limitations in predicting nonlinear data, the application of an attention mechanism was found to enhance prediction performance by effectively learning temporal dependencies and vibration patterns. The experimental results show that the EKF-based estimation achieved a precision of RMSE 1.6 mm, a maximum error of 12.6 mm, and a maximum standard deviation of 3.9 mm compared to actual measurements. From the perspective of experimental design, the proposed hybrid model was able to predict the trajectory of the autonomous agricultural tractor with significantly reduced external disturbances when compared to the actual measured Rear 3-Point coordinates, while also complying with the ISO 12188 standard. These findings suggest that the proposed approach provides an effective and integrated solution for developing high-precision autonomous agricultural systems. |
| format | Article |
| id | doaj-art-9f3e46b7bba84336b4cec25a2b1b6835 |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-9f3e46b7bba84336b4cec25a2b1b68352025-08-20T02:45:52ZengMDPI AGAgriculture2077-04722025-07-011514147510.3390/agriculture15141475Tracking Accuracy Evaluation of Autonomous Agricultural Tractors via Rear Three-Point Hitch Estimation Using a Hybrid Model of EKF TransformerEun-Kuk Kim0Tae-Ho Han1Jun-Ho Lee2Cheol-Woo Han3Ryu-Gap Lim4Agriculture Machinery Test Team, Korea Agriculture Promotion Agency, Iksan 54667, Republic of KoreaAgriculture Machinery Test Team, Korea Agriculture Promotion Agency, Iksan 54667, Republic of KoreaAgriculture Machinery Test Team, Korea Agriculture Promotion Agency, Iksan 54667, Republic of KoreaDepartment of Agriculture Life Bio System, Korea Polytechnic College, Kimje 54352, Republic of KoreaDepartment of Convergent Biosystems Engineering, Sunchon National University, Suncheon 57922, Republic of KoreaThe objective of this study was to improve measurement accuracy in the evaluation of autonomous agricultural tractor performance by addressing external disturbances, such as sensor installation errors, vibrations, and heading-induced bias that occur during the measurement of the conventional rear three-point hitch (Rear 3-Point) system. To mitigate these disturbances, the measurement point was relocated to the cab, where external interference is comparatively minimal. However, in compliance with the ISO 12188 standard, the Rear 3-Point system must be used as the reference measurement point. Therefore, its coordinates were indirectly estimated using an extended Kalman filter (EKF) and artificial intelligence (AI)-based techniques. A hybrid model was developed in which a transformer-based AI model was trained using the Rear 3-Point coordinates predicted by EKF as the ground truth. While traditional time-series models, such as LSTM and GRU, show limitations in predicting nonlinear data, the application of an attention mechanism was found to enhance prediction performance by effectively learning temporal dependencies and vibration patterns. The experimental results show that the EKF-based estimation achieved a precision of RMSE 1.6 mm, a maximum error of 12.6 mm, and a maximum standard deviation of 3.9 mm compared to actual measurements. From the perspective of experimental design, the proposed hybrid model was able to predict the trajectory of the autonomous agricultural tractor with significantly reduced external disturbances when compared to the actual measured Rear 3-Point coordinates, while also complying with the ISO 12188 standard. These findings suggest that the proposed approach provides an effective and integrated solution for developing high-precision autonomous agricultural systems.https://www.mdpi.com/2077-0472/15/14/1475autonomous agricultural tractorextended Kalman filtertransformer modelattention mechanismartificial intelligencerear 3-point hitch |
| spellingShingle | Eun-Kuk Kim Tae-Ho Han Jun-Ho Lee Cheol-Woo Han Ryu-Gap Lim Tracking Accuracy Evaluation of Autonomous Agricultural Tractors via Rear Three-Point Hitch Estimation Using a Hybrid Model of EKF Transformer Agriculture autonomous agricultural tractor extended Kalman filter transformer model attention mechanism artificial intelligence rear 3-point hitch |
| title | Tracking Accuracy Evaluation of Autonomous Agricultural Tractors via Rear Three-Point Hitch Estimation Using a Hybrid Model of EKF Transformer |
| title_full | Tracking Accuracy Evaluation of Autonomous Agricultural Tractors via Rear Three-Point Hitch Estimation Using a Hybrid Model of EKF Transformer |
| title_fullStr | Tracking Accuracy Evaluation of Autonomous Agricultural Tractors via Rear Three-Point Hitch Estimation Using a Hybrid Model of EKF Transformer |
| title_full_unstemmed | Tracking Accuracy Evaluation of Autonomous Agricultural Tractors via Rear Three-Point Hitch Estimation Using a Hybrid Model of EKF Transformer |
| title_short | Tracking Accuracy Evaluation of Autonomous Agricultural Tractors via Rear Three-Point Hitch Estimation Using a Hybrid Model of EKF Transformer |
| title_sort | tracking accuracy evaluation of autonomous agricultural tractors via rear three point hitch estimation using a hybrid model of ekf transformer |
| topic | autonomous agricultural tractor extended Kalman filter transformer model attention mechanism artificial intelligence rear 3-point hitch |
| url | https://www.mdpi.com/2077-0472/15/14/1475 |
| work_keys_str_mv | AT eunkukkim trackingaccuracyevaluationofautonomousagriculturaltractorsviarearthreepointhitchestimationusingahybridmodelofekftransformer AT taehohan trackingaccuracyevaluationofautonomousagriculturaltractorsviarearthreepointhitchestimationusingahybridmodelofekftransformer AT junholee trackingaccuracyevaluationofautonomousagriculturaltractorsviarearthreepointhitchestimationusingahybridmodelofekftransformer AT cheolwoohan trackingaccuracyevaluationofautonomousagriculturaltractorsviarearthreepointhitchestimationusingahybridmodelofekftransformer AT ryugaplim trackingaccuracyevaluationofautonomousagriculturaltractorsviarearthreepointhitchestimationusingahybridmodelofekftransformer |