## 4 Results  
### Experimental Evaluation and Results  
The simulated scenarios were drawn and made on routes that stretched over a distance of about 6 km several times, set according to the flow of vehicles and traffic congestion, and there was a greater flow of pedestrians (see Figure 4). In the creation of the scenarios, unforeseen events such as blockages were taken into account and therefore the measurements lasted for about 25 days with most results on weekends. The crowded times were around noon, between 11:00 and 14:00. The distance travelled of approximately 6 km took place on the crowded streets in Suceava near the campus of Stefan cel Mare University, more precisely on Stefan cel Mare Boulevard and on the connecting streets George Enescu and Universitatii Street, with a capacity of flow and transit of between 800 and 1300 cars up to the maximum degree of congestion [61]. Out of all the outlined scenarios, approximately 10 were selected, those most loaded with information and lasting more than 180 s, in order to analyze a larger sector of the road and to highlight as many elements as possible. In all these scenarios, weather conditions are tested, e.g., sunlight falling perpendicular to the road surface which automatically disrupts the measurements, the speed of the vehicle or the interleaving of objects in the detection plane, the clothing of pedestrians. All these features are analysed gradually increasing both the flow of pedestrians and the speed of movement in order to identify the progress of the first developed version of the proposed system. The first four scenarios, presented in Figure 5, are performed on secondary alleys and streets that do not contain many elements that are difficult to identify, ideal for outlining training sets. The speed in these scenarios is between 15 km/h and 20 km/h, an ideal speed for detection, but in these conditions any negative event can be remedied in a timely manner without endangering the lives of traffic participants or pedestrians. Figure 4: The route on which the scenario simulations took place. Figure 5: Pedestrian detection and predictability movements. Red grids show a dangerous location and green grids show a secure location or position for pedestrians. After the completed stage, to a large extent as expected, there are three scenarios that take place on a route with increased density of vehicles, pedestrians, traffic lights and people who do not have distributive attention and throw themselves into the process of crossing the streets without care. At this stage, the results were as expected and there were no technical problems, apart from that created by sunlight that in some areas obscured the measurements and identification of pedestrians by covering them with light reflection either from asphalt or car windshield. The last set of scenarios increases the degree of difficulty because of the complexity of the route but also the density of vehicles and pedestrians. The speed of travel increased, reaching 65 km/h speed in some sections, at which the identification and predictability process have intermittencies and deficiencies in finding the necessary characteristics. The first part of this scenario takes place on a poorly circulated secondary alley. The moment the car travels on the main road, the first annoying factor is sunlight, which constantly falls on the surface of the windshield, hood, and road surface. This factor disrupts both the movement and the detection process, during which time no pedestrians were identified. After turning the car on an ideal route, we notice that as the speed increases the analysis and the detection of pedestrians is delayed; the speed was between 55 km/h and 62 km/h see Figure 6. In some cases, detection was made, but the identification from the image seems to be delayed. This problem is caused by the hardware component.