LSTM‐based deep learning framework for adaptive identifying eco‐driving on intelligent vehicle multivariate time‐series data

Abstract In the context of automated driving, the connected and automated vehicles (CAVs) technology unlock the energy saving potential. This paper develops an LSTM‐based deep learning framework for eco‐driving adaptive identification on Intelligent vehicle multivariate time series data. The framework can be adapted for Driver Assistance Systems (DAS) to reduce fuel consumption. Specifically, considering overtaking on rural road is a critical maneuver for operation and has potential to reduce consumption, a simulated driving experiment with 30 participants was conducted to collect the multivariate time series data of the overtaking operation behaviors in conditional automation driving. Driving behaviors were classified into eco‐driving operation behaviors and high fuel consumption operation behaviors based on fuel consumption calculated by using vehicle specific power (VSP). Significance analysis based on linear regression was adopted to identify operation behaviors, and an eco‐driving behavior identification model was established with the use of long short‐term memory (LSTM) for multivariate classification theory. Meanwhile, the other four classification algorithms were used to establish identification models for comparison. The results indicated that the gear position, lane position, the acceleration pedal depth, the clutch pedal depth, and the brake pedal depth had a significant influence on fuel consumption. The eco‐driving behavior identification model of overtaking demonstrated a high classification power and robustness with a classification accuracy of 89.16%. According to the simulation results, the developed adaptive identification model is with promising performance. The conclusions provide theoretical support for developing an adaptive strategy for connected eco‐driving.