## 8 Discussion  
Sensors such as cameras and LiDAR systems play a critical role in the perception system of a unmanned vehicle and have a significant impact on its overall performance and capabilities. Both vision and laser scanners installed on the TAERO vehicle are described in terms of parameters and functionality considering the unmanned mode in an off-road environment.  
Cameras are used to gather visual information about the vehicle's environment. They can be used to detect and track objects, to identify and classify objects, and to support navigation and obstacle avoidance. Laser sensors measure time between the emission of the laser pulse and receiving the reflection to determine distance and position. LiDAR systems provide high-resolution data but are limited by their range and susceptibility to weather conditions, while radar sensors have a longer range and better weather resistance but lower resolution. Choosing the right type of sensors for a particular application can have a significant impact on the performance of the perception system in an operational environment.  
According to performance tests carried out on a paved road, the characteristics of the average braking distance to the obstacle (located in front of the vehicle) were determined. Considering the TAERO vehicle introduced herein, the formula to calculate the braking distance for a vehicle is expressed by:  
\[s_{c}=s_{1}+\frac{v^{2}}{2a}, \tag{1}\]  
Figure 16: Object classification from the daylight thermal camera using the YOLO v5 network.  
where:  
\(s_{\text{c}}\)--the braking distance in meters;  
\(s_{\text{1}}\)--brake reaction distance;  
\(v\)--the initial velocity of the vehicle in meters per second;  
\(a\)--the coefficient of friction between the tires and the road surface.  
The braking distance is closely related to dynamic characteristics the vehicle and the response time of the system. Taking into consideration the dependence described above, the braking distance relative to the vehicle speed was measured as shown in Figure 17.  
Object classification in the autonomous mode is based on real-time information received from the perception system. Therefore, it is essential to determine the usefulness of the installed laser scanners in terms of obstacle recognition and classification in the UGV's operational environment. In our analysis, we selected an example of a typical obstacle in the off-road scenario--a tree trunk with a height of 2 m and a diameter of 0.6 m corresponding to the TAERO collision zones, as shown in Figure 18.  
Figure 17: Anticollision system: obstacle detection (**a**) and braking distance as a function of speed (**b**).  
Figure 18: Detection of a tree trunk (**a**) as an obstacle and measurements from the installed LiDAR system (**b**).  
The number of measurement points per defined surface was investigated in terms of object classification using available algorithms. Several speed values with corresponding point densities are shown in Figure 19 to compare the two types of laser scanners used in this research (Ouster and Velodyne).  
The presented study demonstrates the usefulness of a configured perception system up to a speed of approx. 2.8 m/s, taking into consideration the minimum number of points (100) required to classify an object such as a tree trunk [31; 32]. In the case of higher speeds, the used laser scanners provide only a few points per defined surface area, which makes it impossible to define spatial relationships in an off-road environment due to the presence of dense vegetation.