Real-time risk assessment framework for tractors: part 2

We present a behaviour-based, real-time estimator of tractor rollover risk suitable for embedded use. A four-degree-of-freedom vehicle model generated labelled scenarios across speeds (0.5–4.0 m/s), slope angles (0–20 °), and ISO 8608 roughness classes. Two closed-form discriminants for pitch and roll require only three onboard signals—vehicle speed, vertical acceleration, and a 0.3 s double-integrated angular-acceleration angle. On simulated runs, the pitch discriminant achieved 96.4 % sensitivity and specificity; the roll discriminant reached 100 % and 99.5 %, with mean lead times of 0.88 s and 0.53 s before rollover onset. Discriminant scores are mapped to a unified Risk Point scale, enabling interpretable warnings for proactive operator assistance during field and road operation.

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Publication Details

Journal
Engineering in Agriculture Environment and Food
Published
2026-09-29
DOI
https://doi.org/10.37221/eaef.19.3_153
Primary Topic
Agriculture and Farm Safety
Type
article
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article

Real-time risk assessment framework for tractors: part 2

Yuya Aoyagi, Masami MATSUI, Marisa OGINO, Kazuma OHNEDA
Engineering in Agriculture Environment and Food
Agriculture and Farm Safety
article

Real-time risk assessment framework for tractors: part 2

Yuya Aoyagi, Masami MATSUI, Marisa OGINO, Kazuma OHNEDA
article en

Abstract

We present a behaviour-based, real-time estimator of tractor rollover risk suitable for embedded use. A four-degree-of-freedom vehicle model generated labelled scenarios across speeds (0.5–4.0 m/s), slope angles (0–20 °), and ISO 8608 roughness classes. Two closed-form discriminants for pitch and roll require only three onboard signals—vehicle speed, vertical acceleration, and a 0.3 s double-integrated angular-acceleration angle. On simulated runs, the pitch discriminant achieved 96.4 % sensitivity and specificity; the roll discriminant reached 100 % and 99.5 %, with mean lead times of 0.88 s and 0.53 s before rollover onset. Discriminant scores are mapped to a unified Risk Point scale, enabling interpretable warnings for proactive operator assistance during field and road operation.

Engineering in Agriculture Environment and FoodVol. 19(3)
Utsunomiya University (JP), Tokyo University of Agriculture and Technology (JP)
Climate action
Openalex Percentile: Top 14%
Agriculture and Farm Safety
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