A Scoping Review of Energy-Efficient Driving Behaviors and Applied
State-of-the-Art AI Methods
- URL: http://arxiv.org/abs/2403.02053v1
- Date: Mon, 4 Mar 2024 13:57:34 GMT
- Title: A Scoping Review of Energy-Efficient Driving Behaviors and Applied
State-of-the-Art AI Methods
- Authors: Zhipeng Ma, Bo N{\o}rregaard J{\o}rgensen, Zheng Ma
- Abstract summary: There is no comprehensive investigation into energy-efficient driving behaviors and strategies.
Many state-of-the-art AI models have been applied for the analysis of eco-friendly driving styles, but no overview is available.
This paper conducts a thorough literature review on ecological driving behaviors and styles and analyzes the driving factors influencing energy consumption.
- Score: 2.765388013062202
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The transportation sector remains a major contributor to greenhouse gas
emissions. The understanding of energy-efficient driving behaviors and
utilization of energy-efficient driving strategies are essential to reduce
vehicles' fuel consumption. However, there is no comprehensive investigation
into energy-efficient driving behaviors and strategies. Furthermore, many
state-of-the-art AI models have been applied for the analysis of eco-friendly
driving styles, but no overview is available. To fill the gap, this paper
conducts a thorough literature review on ecological driving behaviors and
styles and analyzes the driving factors influencing energy consumption and
state-of-the-art methodologies. With a thorough scoping review process, the
methodological and related data are compared. The results show that the factors
that impact driving behaviors can be summarized into eleven features including
speed, acceleration, deceleration, pedal, and so on. This paper finds that
supervised/unsupervised learning algorithms and reinforcement learning
frameworks have been popularly used to model the vehicle's energy consumption
with multi-dimensional data. Furthermore, the literature shows that the driving
data are collected from either simulators or real-world experiments, and the
real-world data are mainly stored and transmitted by meters, controller area
networks, onboard data services, smartphones, and additional sensors installed
in the vehicle. Based on driving behavior factors, driver characteristics, and
safety rules, this paper recommends nine energy-efficient driving styles
including four guidelines for the drivers' selection and adjustment of the
vehicle parameters, three recommendations for the energy-efficient driving
styles in different driving scenarios, and two subjective suggestions for
different types of drivers and employers.
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