# Attention Horizon as a Predictor for the Fuel Consumption Rate of Drivers
## 1 Introduction
One example of this type of work is [28], where it is proposed that the jerk (derivative of acceleration) measurement can be connected to dangerous driving behavior, and it is integrated to a mobile app that gives correction feedback to the driver. Driver and vehicle monitoring has already been widely adopted in commercial fleets. It quantifies important aspects of vehicle operation and provides feedback loops to various actors, such as the driver or fleet operator. The general belief is that the drivers will adapt their driving style in order to obtain more favorable feedback from these systems. The fleet owner will, in return, see reduced operational costs due to lower fuel consumption and less wear and tear on the vehicles. It is inherently hard to make these systems fair, which is a significant problem. In the trivial case of comparing average fuel consumption between drivers, it is obvious that drivers who are assigned routes with higher average speed or more hilly terrain will score lower, since those two environmental conditions cause higher average fuel consumption. Therefore, it is essential to remove the impact of external conditions, such as specific route or vehicle features, from the eco-driving score when evaluating driver performance. Another possible option is to ensure that drivers are evaluated in very specific conditions in order to ensure uniformity and thus fairness. In this paper, we introduce a new driver performance metric called _attention horizon_ (AH). This metric is automatically calculated based on the onboard collected data, namely, the sequence of the acceleration and brake pedals positions before stopping at a red traffic light. The goal is to measure the awareness of the drivers with respect to the traffic conditions surrounding them. More specifically, the AH measures how far ahead drivers plan, in particular, as they start to take actions to stop the vehicle at a crossing. The key contribution of this paper is introduction of a novel descriptive driver behavior indicator, inspired by complex concepts such as \"attention\", \"awareness\", or \"carefulness\", which is nevertheless possible to compute fully automatically, at a large scale, based on IoT data. We suggest that drivers with higher AH have more eco-friendly driving styles, which intuitively should be correlated with improved fuel efficiency. This is especially important for hybrid or electric vehicles, where harsher braking prevents optimal energy regeneration. In this study, we take advantage of naturalistic data from everyday use of heavy-duty vehicles, specifically city and coach buses, and demonstrate a meaningful correlation between the AH of a driver and their normalized fuel consumption rate (FCR) obtained during weekly and monthly periods. The rest of this paper is organized as follows. First, our acquired data and methodology to obtain AH is described in Section 2. Then, Section 3 presents our approach to validate the correlation between AH and FCR, and describes the results. We discuss our findings and possible future work in Section 4. Finally, we conclude the paper in Section 5. Related Work  
Recently, [PERSON] et al. have introduced an approach that analyzes the driving behavior based on the extracted sequence of drivers' actions [29]. These actions are clusters extracted by a Gaussian mixture model from a 2D histogram (APPES) of accelerator pedal position (APP) and engine speed (ES). The clusters are represented by letter symbols, and additional symbols are defined for braking and automatic cruise control. They show that by combining their sequential feature and a support vector machine (SVM) regressor, they can predict the fuel consumption of a trip with less than 5% error for more than 60% of their samples when using the whole length of the trip. In another study, [30], [PERSON] et al. take advantage of the GPS, accelerometer, magnetometer, and gravity sensors in mobile phones to detect different bad behaviors of drivers that could lead to extra emissions and fuel consumption.