Abstract
Onboard eco-driving systems provide drivers with real-time information about their driving behavior and road conditions, encouraging them to optimize their driving speed and consequently reduce fuel consumption and emissions. However, there are barriers to making eco-driving a habit. To determine the elements that influence drivers’ intentions to practice eco-driving and their acceptance of eco-driving technology, the research team developed a theoretical model based on established theories on planned behavior, technology acceptance, and personal goals. The findings showed that drivers’ intention to practice eco-driving has an indirect effect on their intention to use the system via the factor of perceived ease of use. The research team also explored how cognitive distraction while using an eco-driving system can be a potential barrier to acceptance. The intent is to put forward a solution to improve drivers’ usage of eco-driving by turning off guidance when the system detects that the driver is experiencing serious distraction. To investigate how to detect a driver’s cognitive distraction status when they are interacting with an eco-driving system, this project used a driving simulator and leveraged machine learning algorithms to classify drivers’ attentional states. The findings showed that the glance features played a more important role than the driving features in cognitive distraction.
