Abstract
Within the European research project ecoDriver, the Institut für Kraftfahrzeuge (ika) of RWTH Aachen Univer-sity has conducted user acceptance evaluations of energy efficient driver assistance systems encouraging and supporting drivers in green driving. The project uses the latest achievements in feedback advice strategies and Human Machine Interface (HMI) solutions and takes these as a basis for further development in order to maxim-ise system effectiveness and acceptance. Within the process of research of the ecoDriver project and together with BMW Research and Technology, two studies have been performed using the dynamic driving simulator at ika. The conducted experiments were designed to evaluate compliance with advanced and predictive green driv-ing support systems and HMI concepts. Especially the aspect of anticipatory and predictive driving offers great energy saving potential.
Within the first simulator study, ika has created a setup in order to determine which point in time is most accept-ed by the driver to receive predictive driving advice. Two different deceleration trajectories were compared, of which one lead to a long and one to a short coasting distance. Apart from timing, the system’s interaction with the driver represents another fundamental aspect offering potential for further investigation and improvements with regard to green driving. Therefore, a second driving simulator study focusing on a new and innovative HMI concept created by BMW including feedback on driving behaviour has been performed. Within this study, a baseline system was compared to a system providing feedback on driving by different colours and presenting predictive advice as well as the current CO2 production in order to reduce energy consumption and emissions.
In both studies, participants generally complied with coasting advice when predictive green driving support was provided. The first study revealed that acceptance for short coasting processes is significantly higher. Advising longer distances for coasting does indeed not lead to overall longer distances travelled in coasting mode in com-parison to a short-distance coasting system. Fuel consumption was found to be significantly lower with green driving support systems in both studies. As a side effect of compliance with coasting advice, travel time in-creased significantly.
Different use cases for the system, like speed limit changes, have not been addressed explicitly. Furthermore, the results need to be validated in extended field studies because driving behaviour in simulator studies might differ from in daily life. Besides, a direct comparison of both studies except from general driver behaviour is neither possible nor intended.
Overall, explicit coasting advice enforces green driving behaviour and reduces fuel consumption. However, earlier advice is not always the best option as acceptance might be significantly reduced by long coasting epi-sodes. However, additional feedback on energy efficiency helps to further reduce fuel consumption.
KEYWORDS – user acceptance evaluation, energy efficiency, green driving support systems, human machine interface, driving simulator