# Evaluating the Impact of Drone Signaling in Crosswalk Scenario
## 4 Results
### Assessment of Power and Fuel Consumption  
We are now interested in the influence of driving behavior on fuel consumption along the (500 m) covered distance. We consider the global consumption in liters and the instantaneous consumption in liters per second for the 4 types of drivers already introduced (type 1 to 4).  
Figure 12: Speeds and accelerations of driver type 1, case of wet curved line.  
Figure 13: Speeds and accelerations of driver type 2, case of wet curved line.  
Figure 14 shows the global consumption in liters for the 4 types of driving along the distance of 500 m. It is easy to notice the peak consumption generated by driving characterized by residual accelerations. Normal driving at near-constant speed within the fuel efficiency range of the vehicle saves up to 94% of energy. Abrupt acceleration also results in fuel consumption increases of up to double that amount. We can easily conclude that driving behavior directly influences fuel consumption and thus has an immediate impact on the vehicle's harmful emissions. The use of UAVs allows us to predict this behavior and to assist drivers in reducing the fuel consumption of their vehicle.  
We now represent the energy consumption per second to compare the energy consumption during the three phases (phase 1: observation, phase 2: speed progressive adjusting, and phase 3: braking). We recall that phase 1 reaches from 500 m to 120 m, and phases 2 and 3 together are from 120 m to 5 m. We illustrate the results of phase 1 in Figure 15, for all driver types.  
Figure 14: Fuel consumption for 4 driver types for a distance of 500 m.  
Figure 15: Instantaneous fuel consumption during phase 1 for all driver types.  
From Figure 15, we can see that driver type 3 has the highest instantaneous fuel consumption. As a consequence, the acceleration is the main parameter behind fuel consumption (and not the speed). Indeed, even though driver type 2 has the greatest speed, its fuel consumption is close to driver type 1 because their acceleration profiles are similar.  
We are now interested in phases 2 and 3 (the stopping process). We illustrate the corresponding results for driver type 1 (as an example) in Figure 16. In this figure, we emphasize the fuel consumption for dry straight line, wet straight line, and curved line. In fact, the main impact of the road type is observed during the stopping process. In addition, we consider cooperative driver (dash lines) and non-cooperative driver (solid lines). The former corresponds to a driver that reacts to the first drone alert, whereas the latter corresponds to a driver that does not take into account the drone signaling.  
First, we can see that, during phases 2 and 3, the deceleration is close to zero, compared to phase 1. Consequently, the fuel consumption is lower, and most of the energy is consumed in phase 1, as expected. In addition, during phase 2, the consumed energy in the case of cooperative driver is lower than the case of non-cooperative driver (and this holds for all the investigated road types). Hence, drone signaling helps the driver to reduce the car impact on the environment.