## 1 Introduction
Since the alarms are based on the worker's vision, hazards out of the view may not be detected. An object detection algorithm using a VLP-16 LiDAR was developed by [PERSON] et al. [12], which used the voxel-based plane algorithm. The pedestrian detection approach was presented by [13] through analyzing the temporal changes of grids in an occupancy map. However, variations of the size of the detected object size in the map may influence the performance of the tracking filter negatively. Although the study has the above limitation and the considered application is not construction equipment, it provides a useful reference in tracking pedestrians that can be equivalent to workers around construction machines. [PERSON] [14] proposed a laser scanner-based method to detect and track pedestrians. The limitation of this methodology is that its classification criteria do not cover all the objects and may not be suitable for tracking vehicles or cyclists. The method of utilizing video frames was proposed by [15] to detect workers and mobile machines, including construction equipment, and to predict their motions by applying a Kalman filter. The limitation of this study is that the proposed tracking filter works with linear states only. A multi-object tracking and management system was proposed in [16], which integrates the unscented Kalman filter (UKF) with joint probabilistic data association (JPDA) and the interacting multiple model filter (IMM). A safety system that can generate warnings to avoid collisions in the excavator was presented by [17], using 2D laser scanners and new safety indices. However, the use of 2D laser scanners restricts the vertical position of the detected objects. Another restriction of this method is that this type of laser scanner allows 270 degree-FOV (field-of-view) and hence, two scanners are needed to meet the full coverage of 360 degrees. The fusion of two laser scanners makes the system expensive and complicated due to the integration of their data. The majority of prior studies in the area of safety management focused on proximity-detection and utilization of current states of the detected objects that may not be comprehensive in capturing potential safety risks. For example, the proximity between construction equipment and workers may not be used to indicate potential unsafe conditions if a manipulator or vehicle remains stationary (i.e., does not move) or moves rapidly despite sufficient safe margins. Therefore, quantitative and predictive metrics are highly necessary to assess unsafe conditions and potential risks in fully automated excavators. To tackle the above issue, this study proposes 3D Lidar-based safety algorithms for autonomous excavators that can effectively detect, track, and predict object motion around the excavator, and then evaluate the severity of collision risks using the defined risk metric. Section 1 describes the introduction and literature review of state-of-the-art methodologies. Section 2 briefly explains a framework of the proposed methodologies that covers object tracking and safety evaluation. Section 3 presents the sensory data processing and safety index calculations. Section 4 provides experimental results and analysis, and the paper is concluded in Section 5.