## 3 Performance Evaluation of Autonomous Driving Control Algorithm  
### Test Description  
The crawler-type vehicle for the autonomous driving test used a speed sprayer prototype, which is created by the Sungboo Industry Company, as shown in Figure 4(a). Table 2 shows the vehicle's specifications. The vehicle was equipped with the test hardware for autonomous driving, including a \(GNSS\) antenna, a GNSS-RTK module, a motion sensor module, an embedded board, and an LTE communication device (Figure 4(b)). For the GNSS-RTK module, u-blox ZED-F9P, which provided centimeter-level accuracy \(GNSS\) performance based on u-blox multi-band RTK technology [23] is used. The price of the ZED-F9P was about $170. The configuration of the \(GNSS\) was set to receive GPS, GLONASS, Galileo, BeiDou and QZSS. The positioning method of the u-blox ZED-F9P was set to the single baseline GNSS RTK mode and the output rate of the GNSS-RTK data was set to 5 Hz. MTi-1, which contained a 3-axis gyroscope, a 3-axis accelerometer, and a 3-axis magnetometer [24], was applied as a motion sensor, and the price was about $150. The output rate of the motion sensor's raw data was set to 100 Hz. Moreover, an embedded board, the raspberry pi 4 model, was used to operate the autonomous driving control algorithm [25].  
The autonomous driving test was conducted to investigate the accuracy of the path-following in a parking lot of the Sungboo Industry Company, where the \(GNSS\) signal reception environment is normal. The control interval of the proposed algorithm was set to 0.01 s. Since the speed sprayer runs at a lower speed in spraying pesticides, the maximum and the minimum speed were set at 6 km/h and 0.36 km/h, respectively. The acceptance radius for switching waypoints and the radius for searching a target point were set to 0.4 and 2 m, respectively. The data for generating waypoints was acquired by manually driving a vehicle using a wireless remote controller based on the path-generation algorithm that was developed by [11]. The shape of test paths was defined based on the shape of working paths for the agricultural vehicle, such as a speed sprayer and a weeding vehicle, to which autonomous driving will be applied. In the generation of waypoints, the distance between adjacent points and the angle between successive lines were set to 0.5 m and 3 degrees, respectively. The path-following error for the performance evaluation was calculated as the shortest distance between the vehicle's location and a straight line between two consecutive waypoints near the vehicle's location at every epoch (Figure 6). The path-following error (\(e\)) is calculated as:  
\[e=c\sin\theta \tag{12}\]  
\[\theta=\cos^{-1}\left(\frac{c^{2}+a^{2}-b^{2}}{2 ca}\right) \tag{13}\]  
where \(a\) is the distance between two consecutive waypoints near the vehicle's location, \(b\) and \(c\) are the distance between the vehicle's location and the waypoint near the vehicle's location.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Contents** \\ \hline Drive system & Crawler \\ Dimension (length \(\times\) width \(\times\) height, mm) & 2183 \(\times\) 1300 \(\times\) 1241 \\ Engine & 48 V AC motor \\ Crawler Track (length \(\times\) width \(\times\) tread, mm) & 1100 \(\times\) 200 \(\times\) 1100 \\ \hline \end{tabular}
\end{table}
Table 2: Vehicle specifications.  
Figure 5: Vehicle and hardware used for autonomous driving: (**a**) vehicle; (**b**) hardware.  
Figure 6: Path-following error.