**7. Conclusions**

The workload estimation methods for autonomous driving of work vehicles are proposed and evaluated. A commercial electric robo-mower is used for the

experiments. Specifically, the task to recognize ground conditions, including grounds with long lawn grasses, short lawn grasses, or no lawn grasses, is handled by analyzing data obtained from sensors attached to the robo-mower. Two AI-based algorithms, namely, an RF algorithm and a SNN are proposed. A sensor fusion problem is defined and solved to determine the best combination of sensor data from ten different sensor types. The RF algorithm consisting of 1,000 decision trees and the SNN with only one hidden layer are implemented and evaluated on observation data obtained from various grass cutting field experiments. The RF algorithm achieves 92.3% correct estimation ratio on sensor fusion data in several experiments, while the SNN achieves 95.0%. Furthermore, the accuracy of the SNN is 94.0% in experiments where sensing data are continuously collected as a data stream in real time while the robo-mower is operating. Presently, the proposed estimation system is being developed by integrating two motor control systems into a robo-mower, one for grass cutting and the other for the robot's mobility.

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