## 9 Conclusions  
This paper presents the results of the examination of typical perception sensors installed on a mobile platform to be implemented in autonomous mode or to support remote-control operation in an off-road environment. Manned-unmanned vehicle TAERO was introduced as platform capable to be used in various terrain. This type of vehicle has the flexibility to be operated either by a driver or be controlled in autonomous mode, depending on the mission requirements (evacuation of injured soldiers or civilians from a risky area, transport of goods and equipment to remote locations, or even reconnaissance).  
Sensors used in off-road environments face unique challenges compared to urban environments. Operational conditions can often be shrouded in dust, fog, or other conditions that limit visibility. We performed tests in a typical operating environment with trees and vegetation. Our analysis shows that recognizing obstacles LiDAR point clouds can be challenging in dense vegetation, making it difficult to identify and classify objects using algorithms. Advanced data processing helps to extract meaningful information about objects in the environment, but accuracy is related to the resolution of the laser sensor. Higher-resolution scanners are capable of generating finer and more detailed point clouds, which can provide additional information about the objects in the operational environment. Taking into account the braking properties of the vehicle and the obstacle detection zone, it is possible to effectively implement autonomous mode up to a speed of 2.8 m/s in an unstructured environment.  
Figure 19: Number of points from LiDAR considering object dimensions (W \(\times\) H): 0.6 m \(\times\) 2.0 m.  
Vision sensors such as cameras can also face several challenges in recognizing objects in environments with dense vegetation, such as forests or agricultural fields. Vegetation can occlude objects from the view of the camera, making it difficult or impossible to recognize (hidden behind the foliage). According to our investigations, typical artificial algorithms such as YOLO v5 are not suitable for recognizing objects in off-road environments with vegetation. More advanced and trained computer vision neutral networks are required to accurately recognize objects in these types of environments with vegetation.  
Our research considering a real operational environment highlights the importance of extending perception system architecture to analyze material properties and density. In order to operate effectively in off-road environments with vegetation, it is necessary to install specially designed sensors that penetrate certain materials and detect the reflection of the signals. This will be the next step of our work, which will involve testing wideband radar systems in various scenarios and conditions to determine their ability to accurately detect and analyze material density in the off-road environment.  
Conceptualization, M.N.; Methodology, M.N.; Validation, M.N. and J.K.; Formal analysis, M.N.; Resources, M.N.; Supervision, M.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by research work no. 55.2022489.PL at the Military Institute of Armoured and Automotive Technology.  
The authors acknowledge the members of the consortium that developed the TAERO vehicle (WITPIS, STEKOP, AutoPodlasie, and AP Solutions) for enabling tests in operational environment.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.