Evaluation of driving behavior and the efficacy of a predictive eco-driving assistance system for heavy commercial vehicles in a driving simulator experiment

Driving style is a key factor when it comes to real-world fuel economy. Advanced driver assistance systems (ADAS) hold the potential to support drivers in reducing fuel consumption. In this regard, predictive analysis of the road profile is a highly promising approach. This paper describes the methodology and results of a driving simulator experiment evaluating the efficacy of such a predictive eco-driving assistance system (EDAS). Furthermore, behavioral aspects are another object of the study. The analysis of a linear mixed model (LMM) suggests that an instruction to drive economically could by itself yield savings of about 6.0 % without any kind of additional support. Using the EDAS reduces fuel consumption further by nearly 6.6 % and thus by as much as 12.2 % compared to normal driving. Additionally, the experiment provided support for the hypothesis that an EDAS can induce learning effects in drivers regarding economical driving.