Intelligent Driver System for Improving Fuel Efficiency in Vehicle Fleets

A viable solution for increasing fuel efficiency in vehicles is optimizing driver behavior. In our previous work, we proposed a data-driven Intelligent Driver System (IDS), which calculated an optimal driver behavior profile for a fixed route. During operation, the optimal behavior was prompted to the drivers to guide their behavior toward improving fuel efficiency. This system was proposed for fleet vehicles mainly because a small increase in fuel efficiency of fleet vehicles has a significant impact on the economy. The system was tested on a portion of the fleet's route (12km) and achieved 9-20% of fuel saving. One limitation of the IDS was that the prompted behavior profile was the same for all drivers. However, the approach of driving is significantly different from driver to driver. Therefore, it is important to capture those differences in the optimal behavior profile creation and prompting. This paper presents the first steps of a modified IDS that incorporates different approaches of drivers in optimal behavior profile creation. This work has three main components: 1) analyzing the capability of scaling our previously proposed IDS to the complete route of the fleet, 2) assessing the capability of identifying different types of driver behavior from data, and 3) proposing an IDS framework for integrating different driver behavior in optimizing driver behavior. Experimental results showed that the existing IDS was able to achieve 26-37% estimated fuel savings on the complete route. Conclusions of the paper are: 1)the existing IDS scaled to longer routes, and 2) It is possible to identify different driver behavior using data.