## 4 Conclusions  
In this study, an autonomous driving control algorithm for a crawler-type vehicle has been proposed, and the autonomous driving test was conducted to investigate the accuracy of the  
Figure 8: Autonomous driving results in the second trajectory: (**a**) driving path with waypoints; (**b**) path-following error with the vehicle’s yaw.  
Figure 9: Autonomous driving results in the third trajectory: (**a**) driving path with waypoints; (**b**) path-following error with the vehicle’s yaw.  
path-following for the proposed algorithm. The proposed algorithm is based on the use of a low-cost GNSS-RTK module and a low-cost motion sensor module, which consists of the positioning algorithm and the path-tracking control algorithm. To provide the stable accuracy and continuity of navigation information, the positioning algorithm that integrates the GNSS-RTK module with a motion sensor, including a 3-axis gyroscope, a 3-axis accelerometer, and a 3-axis magnetometer, is loosely implemented through coupled-mode EKF. The path-tracking algorithm calculates the left and the right track velocity for autonomous driving using waypoints and vehicle's navigational information. The performance evaluation was conducted by using three waypoint-based trajectories that include straight-line sections, curve sections, and in-situ rotation section, in a parking lot. As a result, it was found that the RMS of the path-following error for autonomous driving with respect to three trajectories was smaller than 10 cm, and the maximum path-following error did not exceed 30 cm.  
Based on the tests, it could be concluded that the autonomous driving using low-cost navigation sensors is available so that this study might help and apply the commercialization of an autonomous driving agricultural vehicle. In addition, the proposed algorithm can be an alternative solution to complement the limitation of the camera and the laser-scanner-based autonomous driving technique. Although, the autonomous driving control algorithm and the performance of the path-following algorithm with about a 10-cm level of accuracy were demonstrated in this paper; however, the algorithm is necessary to be improved as follows in the future. Since the performance of the path-tracking control algorithm depends on the performance of the GNSS-RTK/motion sensor integrated positioning algorithm, filter tuning, additional constraints, and the integration of the track speeds of the vehicle are necessary to compute stable and accurate navigational information in the agricultural environment. In order to improve the performance of the path-following in the curve section, it is necessary to add an adaptive search radius for the enclosed-based LOS guidance depending on the turning radius at various curve paths. In addition, adjusting the acceptance radius for switching the waypoint based on the characteristic of waypoints such as stop and go is necessary to be added to the proposed algorithm. Finally, since the performance evaluation of the proposed algorithm was conducted only in the parking lot, numerous performance analysis is necessary considering a wide variety of operating conditions, such as terrain and driving environment.  
Conceptualization, J.-h.H., C.-h.P., T.S.K., and Y.Y.J.; methodology, J.-h.H., J.H.K., and J.L.; software, J.-h.H. and C.-h.P.; validation, J.L., J.H.K., and T.S.K.; formal analysis, J.-h.H., C.-h.P. and Y.Y.J.; investigation, T.S.K., and Y.Y.J.; writing--original draft preparation, J.-h.H. and C.-h.P.; writing--review and editing, J.L. and J.H.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the DGIST R&D Program of the Ministry of Science and ICT (20-IT-09). This work was supported by the DGIST R&D Program of the Ministry of Science and ICT (20-IT-09). The authors declare no conflict of interest.