Machine Learning Based off-Road Vehicle Turn Identification Using Vehicle & GPS Parameters John Deere India Pvt Ltd, INDIA

Format:

Book

Conference/Event

Author/Creator:

Rai, Rohit, author.

Conference Name:

Off-Highway Technical Conference 2025 (2025-11-06 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Identification of different types of turns during field operation of off-road vehicles is critical in the overall vehicle development as it is helpful in identifying and optimizing machine performance, correct duty cycle, fuel economy, stability analysis, accurate path planning, customer usage pattern and designing the critical components, et cetera In this study, a machine learning (ML) based methodology has been developed to detect the off-road vehicle turns using vehicle and GPS parameters. Three most common types of off-road vehicles turn conditions e.g., Straight line, Bulb turn, and Three-Point turn have been considered. Different vehicle parameters (like latitude and longitude, compass bearing, yaw rate, vehicle speed, swash plate angle, engine speed, percent load at vehicle speed, raise lower front and PTO channels) generated during field test have been used here. These vehicle parameters are further processed, analysed and used in ML learning model building. Four ML models e.g., SVM, K-NN, Gaussian Naïve Byes and Random Forest have been used here. Experimental results show that the present ML based methodology can identify most common vehicle turns considered in this study with a good accuracy

Notes:

Vendor supplied data

Publisher Number:

2025-28-0344

Access Restriction:

Restricted for use by site license