Lateral Velocity Estimation Utilizing Transfer Learning Characteristics by a Hybrid Data-mechanism-driven Model

This paper introduces an innovative hybrid approach for estimating vehicle lateral velocity, merging mechanism-based methods with a Long Short-Term Memory (LSTM) neural network. Traditional estimation techniques, which are often susceptible to drift and inaccuracies due to parameter mismatches, fail to effectively adapt to varying driving conditions. Our proposed approach leverages the accuracy of mechanism-based estimates in specific scenarios to feed the LSTM network, creating a data-mechanism-driven solution. To overcome the inherent challenges of data-driven models, particularly concerning data quality and volume, our lateral velocity estimation model incorporates a feature extraction layer alongside a regression output layer. This architecture not only facilitates efficient parameter optimization within the feature extraction phase but also enables targeted retraining of the regression layer, significantly boosting transfer learning capabilities. We validate the robustness and the practicality of transfer learning across different vehicle classes in a simulation environment, showcasing its broad applicability and effectiveness.