Analysis of the sensor architecture for a level 3 autonomy system in tracked military vehicles

Abstract

The article presents an analysis of the sensor system architecture designed for third-level autonomy in full-scale tracked platforms intended for military applications. In particular, it focuses on the use of advanced data fusion, enabling the integration of information from heterogeneous sensors, such as LiDARs, cameras, ToF (time-of-flight) sensors, inertial measurement units (IMUs), radars, and vehicle on-board systems. This configuration ensures a high degree of environmental perception accuracy and reliability in decision-making, which is crucial under the dynamic and demanding terrain conditions typical of combat operations. It also enhances situational awareness. Key aspects of designing the sensory system are discussed in detail, including the optimal selection of sensors, their placement on the tracked vehicle, and the implementation of real-time data fusion algorithms. The analysis covers the evaluation of these technologies in terms of environmental mapping accuracy, operational reliability, and adaptability under varying operational conditions. The research results indicate that an appropriate sensor architecture, supported by advanced data processing methods, significantly improves the effectiveness of condition-based autonomous control and the vehicle ability to adapt to the specific requirements of combat missions. The conclusions drawn from the study provide valuable guidance in designing modern military vehicles that utilize state-of-the-art sensing technologies and autonomous algorithms.