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.

Identifying the Causes of Ship Collisions Accident Using Text Mining and Bayesian Networks

“…When the data are mined in the form of text, it can be called text mining. Of course, this process also requires the use of machine learning, information processing, pattern recognition, database and computer linguistics, and other disciplines of theory and methods [8], [9], [10]. Gupta and Lehal [11] conducted an analysis of the commonly used techniques and application Manuscript received 6 August, 2023; accepted 21 October, 2023. https://doi.org/10.5755/j02.eie.35630 fields of text mining.…”