An LSTM-Based Model to Recognize Driving Style and Predict Acceleration

To ensure safe cooperative driving in mixed traffic with both manned and unmanned vehicles, it is crucial to understand and model the driving styles of human drivers. This paper explores how to develop accurate recognition of driving style and use that for the prediction of vehicle motion, which enables better performance in cooperative driving. A simulation testbed that consists of a driving simulator and a copilot is first introduced for the purpose of data collection and testing. A Long Short-Term Memory (LSTM)-based network that models human driving styles and predicts driving acceleration is developed. Standalone tests are conducted to examine the model performance in the simulation testbed. Finally, the model is evaluated in a series of merging experiments that involves 5 vehicles.