## 1 Introduction  
In underground mining, we observe a dynamic growth in the development of monitoring and analytics, both in the case of stationary and mobile assets. For an LHD machine (Load--Haul--Dump vehicle), the main directions of analytical development are primarily related to the identification of operating cycles, efficiency assessment [1, 2, 3], technical diagnostics [4], identification of dynamic overloads, road condition classification [5], assessment of the driving style of operators, assessment of road conditions [6], and optimization of production [7]. Tracking the workflow of the mining fleet is a key challenge for an underground mine. Taking into account the number of operational contexts, especially the external factors influencing the operational parameters of mining machines, the location of machines in underground mining excavations is crucial for data analysis from monitoringsystems. The solutions available on the market are usually based on GPS and relate to opencast mines. In the literature, we can find several works on the location of assets in underground excavation. Oftentimes, they are based on inertial measurements, odometry, or RFID (Radio-frequency identification) technology. Inertial navigation systems for underground mining have been developed since the mid-1990s [8]. This type of navigation was usually developed with the automatization of LHD machines for strictly defined conditions of deposit exploitation [9; 10; 11]. In practice, the development of inertial navigation for universal use in an underground mine is difficult to achieve, among others due to the phenomenon of gyroscope drift and several disturbances, especially in the magnetometer readings. Such systems are usually based on RFID technology, in which the key is to recognize the entry or exit of a machine from a given mine zone. In practice, gates are most often installed at intersections, and the proposed solution ensures the identification of the machine passing through the intersection, its direction, and the turning angle itself. In the case of exploitation of the deposit based on a room-and-pillar system where the layout of the excavations has a Manhattan structure, achieving precision at the crosscut level is sufficient. The main problem is related to the constant expansion of the extensive communication infrastructure, which is certainly neither cheap nor easy to maintain. In this article, we present a simple and inexpensive approach to the vehicle turn detection problem based on inertial sensors. The proposed algorithm provides the identification of the time moment of the turning maneuver, its direction, and approximate angle. The turn identification algorithm can find a wide range of applications. First of all, it can be used to recognize and correct the LHD motion path based on the gyro integral. In [12], the authors proposed a localization model based on IMU (Inertial Measurement Unit) sensors and a digital map for a room-and-pillar system on the example of KGHM's underground copper ore mine. After minor modifications, the turn detection algorithm can be a significant simplification of the model of the machine location on the digital map. Additionally, identifying the machine's turns at crossroads is particularly important when assessing the operator's driving style. It is necessary to check the operator's behavior, especially if they have followed the manufacturer's recommendations in terms of driving speed. Another application example is the analysis of dynamic overloads and stresses occurring during the turning maneuver in the reliability tests of structural nodes. After the detection of turns, the signal from the IMU coming from these specific signal fragments can be utilized, for example in the problem of driver classification and driving style recognition [13; 14].