Vehicle Acceleration Controller Using Neural Network Prediction

The patent replaces the traditional mechanical calibration approach (physical experiments, manual lookup table creation) with a machine learning-based system. The neural network learns the complex nonlinear relationships between longitudinal force and acceleration through data-driven training, substituting the time-consuming physical calibration process with computational modeling that provides both high precision and real-time performance.

The patent performs preliminary training of the machine learning model using historical drive data during the development phase. This preliminary action creates a pre-trained neural network that already contains learned relationships between force and acceleration, eliminating the need for time-consuming on-site calibration experiments and lookup table generation during vehicle commissioning or operation.

The patent transforms the calibration approach from using static lookup tables with discrete force-acceleration pairs to a continuous machine learning model that processes multiple input parameters (longitudinal force, vehicle mass, friction coefficients, external conditions, temperatures, slopes) simultaneously. This parameter transformation enables the system to handle the complexity of multiple influencing factors through a unified neural network model rather than requiring separate calibration data for each parameter combination.

The patent creates a virtual model (neural network) that copies and represents the complex physical relationships between longitudinal force and vehicle acceleration. Instead of relying on extensive physical experiments to characterize each parameter combination, the neural network learns these relationships from training data and creates a computational copy of the system behavior, simplifying the overall calibration system while maintaining accuracy.