Prediction of brake pedal aperture for automatic wheel loader based on deep learning

Abstract

Complex and changing driving environments not only affect the operating requirements of automatic wheel loader but also threaten its driving safety. Therefore, the automatic wheel loader must adopt appropriate braking strategies to realize accurate control of the brake pedal aperture under certain operating conditions. For the V-shaped operation mode of wheel loader, the operator's operation specification is evaluated using three characteristics: operation time, driving distance and friction work. By combining the driving data of experienced drivers in different driving environments with deep learning, a deep long short-term memory network was constructed to predict the brake pedal aperture for different braking types. The proposed anthropomorphic control method that combines driving data and deep learning can be used to predict the aperture value of the wheel loader brake pedal in complex driving environments. This would enable the braking process to conform to the braking decisions of experienced drivers and thereby meet the operational requirements while ensuring driving safety.