Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review

Int J Environ Res Public Health. 2022 Jun 14;19(12):7310. doi: 10.3390/ijerph19127310.

Abstract

Eco-driving guidance refers to courses, warnings, or suggestions provided to human drivers to improve driving behaviour to enable less energy use and emissions. This paper reviews existing eco-driving guidance studies and identifies challenges to tackle in the future. We summarize two categories of current guidance systems, static and dynamic, distinguished by whether real-world driving records are used to generate behaviour guidance or not. We find that influencing factors, such as the content of suggestions, the display methods, and drivers' socio-demographic characteristics, have varied effects on the guidance results across studies. Drivers are reported to have basic eco-driving knowledge, while the question of how to motivate the acceptance and practice of such behaviour, especially in the long term, is overlooked. Adaptive driving suggestions based on drivers' individual habits can improve the effectiveness and acceptance while this field is under investigation. In-vehicle assistance presents potential safety issues, and visualized in-vehicle assistance is reported to be most distractive. Given existing studies focusing on the operational level, a common agreement on the guidance design and associated influencing factors has yet to be reached. Research on the systematic and tactical design of eco-driving guidance and in-vehicle interaction is advised.

Keywords: eco-driving; human-driving vehicles; literature review; user acceptance.

Publication types

  • Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Accidents, Traffic / prevention & control
  • Automobile Driving*
  • Humans

Grants and funding

This work is funded by the National Natural Science Foundation of China for Young Scholars (grant number 52102409), the Natural Science Foundation of Jiangsu Province for Young Scholars (grant number BK20210246), the EU-funded project MODALES (grant number 815189), and the National Key R&D Program of China (grant number 2021YFE0112700).