Driving power prediction of heavy commercial vehicles based on multi-task learning

With the continuous development of vehicle intelligence technology, developing predictive control strategy has become a research hotspot, and the prediction of vehicle driving power is crucial for developing such strategies. In the field of short-term power prediction, most approaches indirectly predict vehicle driving power by predicting speed, road grade, and other information based on deep learning single task methods. To reduce the calculation scale of the prediction model and solve the multivariable coupling prediction problem required for power prediction, this paper proposes a three-parameter prediction model for road grade, speed, and acceleration based on multi-task learning (MTL) network. The development and validation of the prediction model were based on actual vehicle data. Compared with traditional single-task prediction methods, the proposed model improves the accuracy of power predictions by more than 10%. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)