## 5 Latency Mitigation Strategies
### Perception Latency Approaches  
Perception latency is the delay in the perception system at the remote operator's end. A list of latency mitigation strategies for perception is provided in Table 6. Predictive displays generate the probable vehicle response on the delayed or predicted view based on the current actions of the operator. They have been shown to effectively withstand and compensate for the negative impacts of latency during teleoperation [42]. These methods can be classified as offline or online [129]. The former involves using prior knowledge of static environments and operator behaviour models before making predictions. The latter is suitable for dynamic environments and makes near real-time predictions to provide instantaneous feedback. During remote driving, the operator can receive sensory feedback in three main forms: visual, audio, and haptic [7]. However, most studies do not focus on audio and haptic feedback. Therefore, visual data are significant for the remote operator. Predictive display \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Domain** & **Teleoperation Entity** & **Algorithm** \\ \hline
[82] & Bilateral teleoperation & Adaptive control, position tracking and force feedback & Radial basis function neural network (RBFNN)-based adaptive sliding mode controller and projection mapping by saturation function \\ \hline
[127] & Robotic teleoperation & Control sensory manipulation, haptic feedback & Augmented sensory manipulation based on motor learning and rehabilitation principles, inverse kinematics for human adaptation during delays \\ \hline
[83] & Tele-driving & Control & Model predictive control (MPC) and improvement in cost function \\ \hline
[84] & Tele-driving & Control & Model predictive control (MPC) and model error compensator (MEC), Kalman filter \\ \hline
[128] & Telepresence robot, IoT, healthcare & Delayed control signals & Markov model, deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) \\ \hline
[12] & Robotic teleoperation & Control prediction & Survey of time-series prediction: Statistical approaches and neural network approaches \\ \hline
[88] & Robotic teleoperation & Control & Model-free predictor modified with adaptively varying predictor parameter \\ \hline
[58] & Strategy games & Control prediction & Artificial neural networks with internal states predicting future position of mouse \\ \hline
[64] & Vehicle teleoperation & Control and steering prediction & Non-linear model predictive control (NMPC), successive reference-pose tracking (SRPT) to predict and improve path tracking, reference pose and speed \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._methods mainly focus on visual feedback prediction. However, most of the research on predictive displays uses simulation-based experiments [36]. Earlier offline approaches estimate and predict images that rely on first-order prediction techniques, depending on the system, delays, and actions of the operator [45]. They are restricted to state prediction and future pose estimation. For example, estimating the visual impact of control commands, heading angles, and steering wheel angles, predicting position and trajectory, or by using other dynamic, curvature, and geometric models [52; 129]. However, reliance on geometric models for feature extraction and matching in low-textured scenes may lead to inaccuracies [129]. Moreover, first-order techniques are ineffective for high-latency, high-speed, and long-distance teleoperations and do not include factors related to the uncertainty of future events. Integrating these first-order approaches with recent state-of-the-art approaches that use artificial intelligence (AI) and NNs is challenging [46; 51]. Similar to the model-free control approaches in the previous section, the modern online prediction approaches involve deep neural networks (DNNs). They mainly involve data-driven models for time-series predictions of future-perspective video frames [8].