How I reduce fuel consumption: An experimental study on mental models of eco-driving

Eco-driving has the potential to reduce fuel consumption and therefore emissions considerably. Previous research suggests that drivers have a certain level of eco-driving knowledge and skills, which they refrain from practising in their everyday lives. At the same time misconceptions and ambiguous messages from eco-driving support systems can confuse and demotivate. This research aimed to identify the mental models of eco-driving that regular drivers have. A driving simulator experiment with a varied road layout comprising urban and motorway sections was designed. The study used simple driving task instructions to investigate changes in the participants’ behaviour and thoughts in three conditions. Sixteen drivers were asked to ‘Drive normally’, ‘Drive safely’ or ‘Drive fuel-efficiently’. Behavioural measures, think aloud protocols and interviews were compared and analysed. The emphasis of this study was on eco-driving relevant indicators such as accelerating, braking, coasting and car-following. The results show that the participants do have mental models of eco-driving, which they did not use in the Baseline drive, when they were instructed to ‘Drive normally’. Misconceptions about speed and travel time provide the potential for more effective communication with the driver about the momentary efficient speed as well as resulting time losses and fuel savings. In addition, in-vehicle guidance can increase driving safety compared to practicing eco-driving without them

Analyzing Measures for the Construct “Energy-Conscious Driving”: A Synthesized Measurement Model to Operationalize Eco-Feedback

During the last several years, a large number of studies have dealt with eco-driving and have defined rules for driving vehicles more ecologically, eco-friendly, and energy efficiently. These rules are vague or insufficient for achieving their purpose, and the construct “energy- conscious driving” is unsatisfactorily defined. To structure available research and develop a more extensive concept of energy-conscious driving, a measurement model for energy- conscious driving is introduced. The model stems from a literature review conducted to identify six groups of measures for energy-conscious driving, and a synthesis of these groups to identify dependencies between them. This paper contributes to theory by building on existing knowledge on eco-driving through an analysis of available literature and describing dependencies between our six measures of energy-conscious driving. Based on our model, researchers can evaluate different eco-feedback designs and practitioners can implement more specific eco-feedback systems for improved user performance

Interface design considerations for an in-vehicle eco-driving assistance system

This high-fidelity driving simulator study used a paired comparison design to investigate the effectiveness of 12 potential eco-driving interfaces. Previous work has demonstrated fuel economy improvements through the provision of in-vehicle eco-driving guidance using a visual or haptic interface. This study uses an eco-driving assistance system that advises the driver of the most fuel efficient accelerator pedal angle, in real time. Assistance was provided to drivers through a visual dashboard display, a multimodal visual dashboard and auditory tone combination, or a haptic accelerator pedal. The style of advice delivery was varied within each modality. The effectiveness of the eco-driving guidance was assessed via subjective feedback, and objectively through the pedal angle error between system-requested and participant-selected accelerator pedal angle. Comparisons amongst the six haptic systems suggest that drivers are guided best by a force feedback system, where a driver experiences a step change in force applied against their foot when they accelerate inefficiently. Subjective impressions also identified this system as more effective than a stiffness feedback system involving a more gradual change in pedal feedback. For interfaces with a visual component, drivers produced smaller pedal errors with an in-vehicle visual display containing second order information on the required rate of change of pedal angle, in addition to current fuel economy information. This was supported by subjective feedback. The presence of complementary audio alerts improved eco-driving performance and reduced visual distraction from the roadway. The results of this study can inform the further development of an in-vehicle assistance system that supports ‘green’ driving

Mental models of eco-driving: The measurement and activation of drivers’ knowledge and skills

Eco-driving has the potential to reduce fuel consumption and therefore emissions considerably. Previous research suggests that drivers already possess a certain level of eco-driving capability, which they do not practise in their everyday lives. The studies reported in this thesis are based on a mental models approach, which enabled an in-depth exploration of eco-driving knowledge and skills and broadened the understanding of the underlying cognitive mechanisms. This thesis describes two driving simulator experiments aiming to measure, activate and ultimately interrupt eco-driving mental models in a variety of scenarios, relevant for safe and eco-driving. The studies used simple driving task instructions, text message interventions as well as a workload task. Changes in the participants’ behaviour and thoughts were analysed with a combination of quantitative and qualitative methods. The results suggest that drivers have mental models of eco-driving on several levels, ranging from knowledge and strategies to tactics in specific situations to automated behaviour. However, in the first experiment they did not use them when they were instructed to ‘Drive normally’. In the second study text message primes and advice provided over two weeks were not able to replicate the effect of experimental instructions given directly before driving. Behavioural changes following these instructions were abandoned when performing a workload task, and not resumed afterwards. Future research needs to consider alternative methods to prompt drivers to use their existing eco-driving knowledge and skills. Studies with a larger number of participants, and in real-world settings can then validate findings. It is suggested to expand the mental models approach into other fields such as sustainable transport in general