End-to-end learning for high-precision lane keeping via multi-state model

Abstract

High-precision lane keeping is essential for the future autonomous driving. However, due to the imbalanced and inaccurate datasets collected by human drivers, current end-to-end driving models have poor lane keeping the effect. To improve the precision of lane keeping, this study presents a novel multi-state model-based end-to-end lane keeping method. First, three driving states will be defined: going straight, turning right and turning left. Second, the finite-state machine (FSM) table as well as three kinds of training datasets will be generated based on the three driving states. Instead of collecting the dataset by human drivers, the accurate dataset will be collected by the high-performance path following controller. Third, three sets of parameters based on 3DCNN-LSTM model will be trained for going straight, turning left and turning right, which will be combined with FSM table to form a multi-state model. This study evaluates the multi-state model by testing it on five tracks and recording the lane keeping error. The result shows the multi-state model-based end-to-end method performs the higher precision of lane keeping than the traditional single end-to-end model.