## 1 Introduction  
Arbitrating control authority between a human driver and an automation system is regarded as a crucial function until fully automated control is successfully commercialized [1]. Due to erroneous driving behavior caused by the automatic control system of so-called self-driving vehicles, human drivers still play an important role in conducting maneuvering tasks. Because fully automated driving is focused on removing the human driver from the control loop, it liberates the human driver from tedious maneuvering but, consequently, leads to a loss in driving flexibility. Despite the dramatic development of artificial intelligence and the powerful computational performance of control units, there is not much doubt that it is challenging to completely remove the human driver from the conventional control loop [2, 3]. Over the past few years, many researchers have shown an interest in the teleoperation of road vehicles to counter the uncertainty of a fully automated control system [4]. The typical teleoperation case scenario includes emergency takeover in urban driving and extra services like valet parking. However, it is also undeniable that human drivers also have uncertainty in terms of cognition of circumstances, bad driving performance caused by accumulated fatigue, and unquantifiable mental status properties which account for a large number of traffic accidents [5]. Therefore, it is important to arbitrate the control authority between the vehicle and the teleoperator, especially when wireless communication delay is concurrent. The main reason for the noticeable asynchrony between the teleoperator'scontrol and the actual mobility of the vehicle originates from a large number of network communication delays [6]. This degradation of mobility performance can be dramatic when exposed to environmental conditions such as tunnels with no wireless network coverage, mountainous regions, and malicious attacks such as jamming. Hence, when introducing a teleoperation scheme for an actual road vehicle, communication delays should be continuously monitored and evaluated based on the required safety integrity level in order to avoid hazardous situations. The works of literature regarding teleoperation can be largely classified into human-in-the-loop studies [7; 8; 9; 10] and driving performance evaluations [11; 12; 13]. In fact, numerous articles have already shown that task completion time increases depending on latency, regardless of the variety of target tasks, e.g., pin transfer [14], energy dissection [15], needle task [15], surgery [16], block transfer [17], and so on. In particular, a predictive display approach was introduced to forecast vision information to improve drivers' cognition during teleoperation [18; 19; 20]. What should be noted is that some of the articles report that forecasting camera information considering communication delay can aid in improving human performance and workload while teleoperating a remote target [21; 22]. Thus, methods like the predictive display can reduce the asynchrony between the perception of the teleoperator and the target vehicle's response approach, which requires adaptive state estimation of communication delay. There are two main approaches to handling sample outliers. One is a compensation strategy and the other is a detect-to-reject strategy. First, as the name suggests, the compensation method sets the allowable range of the sample value using a method appropriate for the physical parameter [23; 24; 25]. In general, when using multivariate variables, there is a method for setting a normalized allowable area based on the Mahalanobis distance, and it is common to set a threshold appropriate for other safety standards or purposes [24]. Second, in the case of the detect-to-reject strategy, the outlier detection part of the compensation method is the same, but the stability of the system is improved by removing samples [26; 27].