## 6 Conclusions  
This study proposed sensing algorithms for object tracking and predictive safety evaluation for an autonomous excavator that effectively enables us to avoid potential collision risks between the excavator and the workers around it. Firstly, the proposed method extracted non-ground points from raw point cloud data with 3D LiDAR. The non-ground points were then clustered, and each cluster was fit into a bounding box that was considered as a detected object. The positions and velocities of the detected objects were then estimated and predicted using the integrated tracking filter where the JPDA for multi-target tracking was coupled with UKF and IMM to improve the performance of the tracking management by dealing with the non-linear state estimation and different motion behaviors (i.e., constant velocity and constant turn models), respectively. The developed algorithm can also monitor the current working areas of the excavator and predict them to conduct safety evaluations. The defined safety indices, \(TTC\) and \(x\) were computed through the prediction of the motion of the detected objects and calculation of the working areas of the excavator based on kinematic analysis. The severity of potential collision risks was defined using the safety indices. Validations of the developed algorithms were performed with a modified mini excavator having a VLP-16 3D LiDAR, LVDT stroke sensors, and a rotational encoder. Test results indicate that the proposed predictive safety algorithms can successfully conduct tracking of objects, predict their motion, and provide safety evaluations to prevent a collision accident. A novel feature of the developed safety strategy is the ability to predict the future states of both multiple objects and an excavator within a prediction horizon (i.e., 1 s in advance) during operations. Since previous studies of construction equipment have focused on monitoring its present state, the predictive attribute of this study, conceptualized through a risk metric with time and distance to potential collisions, is the most significant advance, and it will be used to control autonomous excavation equipment that requires more elaborate and precise safety strategies. As part of the evaluation of the developed safety algorithms, this work provides a unique methodology for defining a threshold for judging success or failure predictions and analyzing the success rates of operations with respect to time frame. Therefore, the study proposes an advanced safety management framework that can be applied to many industrial applications, such as construction equipment in transition to full automation and/or autonomy, in which predictive safety is a prerequisite component. As the constant velocity and constant turn models were assumed for the prediction of the moving objects in the tracking filter, the application of the variable velocity and random motion models will assist in improving the performance of the safety algorithms in predicting the nonlinear motions of the objects in future work. System dynamics [25; 26] can also be considered for an extension of the current work to enhance the predictive capability of the developed algorithms by investigating the interrelations among the factors causing safety incidents, such as worker-machine interaction. Excavator safety can be improved by pairing the proposed safety algorithms with the development of sophisticated sensing algorithms or platforms that can avoid the restricted FOV and occlusions caused by obstacles in excavation spaces. Finally, a further advantage of state prediction is that the actuators of an autonomous excavator can be controlled in an adaptive manner depending on the degree of potential collision risk, thereby providing maximum productivity and safety over simply stopping the excavator.