# Advancing Digital Twin-Based Collision Avoidance:
## 2 Collision Prevention Mechanism
### Related Work  
The advancement of the 5G standard, particularly up to 3 GPP Release 18, has significantly contributed to the enhancement of applications across various sectors [3]. This progress is particularly beneficial for collision prevention systems, as illustrated in Figure 1. A key application of this technology is the integration of real-time data into digital representations of industrial environments, leading to the creation of 'digital twins' of real scenarios. Defined as \"a virtual representation of a physical asset, that continuously consumes data from the physical asset, processes it, then provides intelligent feedback to its real counterpart\" [4], digital twins play a crucial role in collision prevention by using real-time data on the position and orientation of forklifts. This system not only supports two-way communication, including collision alerts, but also allows the incorporation of additional details such as the forklift's location and steering direction to create a visual replica of the vehicle. Digital twins are increasingly recognized as effective tools for developing and evaluating complex systems in realistic environments, surpassing traditional simulation methods [4]. A thorough examination of intelligent solutions for enhancing road user safety is presented in the survey by [5]. The survey delves into various collision avoidance systems and ongoing research projects dedicated to applications ensuring the safety of vulnerable road users. The authors explore the potential of leveraging 5G technology to enhance road users' safety, discussing diverse architectural approaches, such as RAN-wide communication and direct communication between vehicles, pedestrians, or the network. Additionally, they address key performance indicators associated with these solutions. In our research, we also focus on collision avoidance scenarios involving vehicles and pedestrians. However, in contrast to the aforementioned work, our emphasis is on solutions tailored to industrial sites. Notably, our investigation accounts for indoor vehicle traffic, which may not have access to a public RAN. The authors of [6] give a review of different kinds of collision avoidance systems. They differentiate sensors of such systems into two categories. Active sensors that are used to detect other vehicles include radar, laser, or lidar. Passive sensors collect data without sending sending a signal. They include cameras, microphones, radar and lidar. The review focuses on road safety and thus does not consider limited space indoor scenarios, as in intralogistics. In such scenarios, the difference is that we can establish additional infrastructure which allows for a new range of solutions. Computer vision systems on vehicles can be used to detect other vehicles, pedestrians or other obstacles. In [7], the authors utilize such a system to detect animals that are entering the road, warning the driver or slowing the vehicle. The authors of [8] propose a neural-network-based system that accurately detects and classifies vehicles, achieving about 95% accuracy. For practical purposes, such systems are very handy if the vehicle already comes equipped with a camera. The advanced in this field are very promising. They are well suited to be used together with an RTLS for increased robustness and reliability. However, high computation power might be necessary, which can lead to high cost. Furthermore, in many industrial areas, there is a high degree of particulate pollution, which requires the cameras to regularly cleaned. The authors of [9] conduct experiments where robots drive around within a 5G campus network between two cells with their own base station. At the same time, the robots transmit their position in real time to an edge server. In their work, they focus on how the handovers affect the data throughput and the latency and how accurate the positioning system is. Their system shows that RTLS works well over 5G, which is an important part of the system we present in this paper.