# Design of Unmanned Road Widths in Open-Pit Mines Based on Offset Reaction Times
## 2 Methodology
### Unmanned Road Analysis  
The current guidelines are unsuitable for the changing needs of future smart open-pit mining and hybrid settings where unmanned and manned vehicles operate, due to the differences in operational models between unmanned and manned driving scenarios. Consequently, there is a need to reassess the current road design guidelines for open-pit mines. Unmanned vehicle networking, remote monitoring, and sensor technologies [12] have revolutionized the traditional driving mode, which relies on human perceptions of the environment. This has reduced the time the vehicle interacts with the environment, increasing its speed. Consequently, this affects the standard for the design speed of open-pit mine trucks [13]. Unmanned vehicles no longer rely on human manipulation. The current unmanned environmental sensing [14], vehicle positioning [15], vehicle scheduling [16], behavioral decision-making [17], and computer control systems form the entire system, which can simulate or even exceed the driver's method of controlling the vehicle [18]. If we reduce the instability and diversity of the driver's control of the vehicle, this can decrease the width requirements for open-pit mine transportation roads. The path decision-making process for unmanned vehicle driving is completed by the control system. When all vehicles are networked, the system will analyze their destinations and road conditions to plan the best driving route. This greatly improves the waiting times for mining trucks stopping at mining and loading points and enhances the overall operational efficiency of the road network [19]. Unmanned driving commonly denotes the technology or system enabling a vehicle to function autonomously. The system comprises an amalgamation of sensors, cameras, AI systems, and additional technologies enabling the vehicle's navigation and reaction to its surroundings. The primary distinction between unmanned and manned (traditional) driving lies in how humans manage the vehicle. During crewed driving, a person takes charge of the vehicle, handling all navigational, speed, and traffic response decisions. Conversely, during autonomous driving, the choices are determined by the vehicle's internal computer configurations, relying on sensor data and preset algorithms. It is essential for human drivers to stay persistently alert to the streets and their environment. Autonomous vehicles depend on ongoing monitoring from sensors and cameras to stay informed about their surroundings, theoretically decreasing the likelihood of incidents related to distractions. The reaction time of human drivers is subject to influence from multiple elements such as tiredness, diversions, and disability. Unmanned vehicles are likely to respond more quickly to unexpected events owing to their sensors' ability to identify and interpret information more rapidly than humans. The advantages and disadvantages of unmanned vehicles relevant to this study are summarized in Table 1. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Advantages** & **Disadvantages** \\ \hline No blind spots, more accurate judgement of distances & Highly affected by bad weather and complicated road conditions \\ \hline Faster and more accurate responses to unexpected situations & Cannot understand some of the potential rules of the road, such as accelerating or decelerating as quickly as possible to avoid parallel driving \\ \hline Never get tired and work more efficiently & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of unmanned vehicles. Based on a prior analysis of the width of the road of an open-pit mine's end slope, the traditional open-pit mine road was given a larger road width and safety distance to mitigate safety accidents caused by the mining area's unique environment, operational driver errors, driver fatigue, vehicle failure, vehicle aging, and other related issues during the production and transportation processes.