R-WhONet: recalibrated wheel odometry neural network for vehicular positioning using transfer learning

作     者：Onyekpe, Uche Szkolnik, Alicja Palade, Vasile Kanarachos, Stratis Fitzpatrick, Michael E. 

作者机构：Research Centre for Computational Science and Mathematical Modelling Coventry University Priory Road CoventryCV1 5FB United Kingdom African Institute for Artificial Intelligence Accra North Legon Ghana University of Birmingham BirminghamB15 2TT United Kingdom Faculty of Engineering and Computing Coventry University Priory Road CoventryCV1 5FB United Kingdom 

出 版 物：《Neural Computing and Applications》 (Neural Comput. Appl.)

年 卷 期：2025年第37卷第9期

页      面：6547-6565页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Inertial navigation systems 

摘      要：This paper proposes a transfer learning approach to recalibrate our previously developed Wheel Odometry Neural Network (WhONet) for vehicle positioning in environments where Global Navigation Satellite Systems (GNSS) are unavailable. The WhONet has been shown to possess the capability to learn the uncertainties in the wheel speed measurements needed for correction and accurate positioning of vehicles. These uncertainties may be manifested as tyre pressure changes from driving on muddy and uneven terrains or wheel slips. However, a common cause for concern for data-driven approaches, such as the WhONet model, is usually the inability to generalise the models to a new vehicle. In scenarios where machine learning models are trained in a specific domain but deployed in another domain, the model’s performance degrades. In real-life scenarios, several factors are influential to this degradation, from changes to the dynamics of the vehicle to new pattern distributions of the sensor’s noise, and bias will make the test sensor data vary from training data. Therefore, the challenge is to explore techniques that allow the trained machine learning models to spontaneously adjust to new vehicle domains. As such, we propose the Recalibrated-Wheel Odometry neural Network, based on transfer learning, that adapts the WhONet model from its source domain (a vehicle and environment on which the model is initially trained) to the target domain (a new vehicle on which the trained model is to be deployed). Through a performance evaluation on several GNSS outage scenarios—short-term complex driving scenarios such as on roundabouts, sharp cornering, hard-brake and wet roads (drifts), and on longer-term GNSS outage scenarios of 30s, 60s, 120s and 180s duration—we demonstrate that a model trained in the source domain does not generalise well to a new vehicle in the target domain. However, we show that our new proposed framework improves the generalisation of the WhONet model to new vehicles in