# TA-RRT*: Adaptive Sampling-Based Path Planning Using Terrain Analysis
## 5 Discussion of Real-World Scenarios
### Sensor Deployment Information  
Firstly, the practical implementation challenges related to sensor deployment can vary depending on the utilization of primary perception sensors. In practice, most research institutions and commercial autonomous vehicles assume the use of cameras as fundamental sensors [26; 27; 28]. However, due to considerations such as cost and real-time performance limitations, LiDAR may not be employed in certain systems [27]. Consequently, this discussion categorizes sensor deployment scenarios into two primary cases: (1) a Camera-Based Perception System, and (2) a LiDAR-Camera Fusion-Based Perception System that integrates data from both LiDAR and cameras. These scenarios are explored further in the subsequent analysis. The Camera-Based Perception System primarily focuses on the generation of camera-based uMaps as well as localization and mapping. The creation of uMaps may involve constructing a 2D map using 2D BEV information derived from cameras [29], or alternatively, generating a 3D map through camera-based 3D reconstruction and landmark detection [30]. Localization and mapping are then performed on the generated uMap using a global positioning system (GPS) and landmark-based references to ensure accurate spatial alignment and navigation. In contrast, LiDAR-based perception systems utilize point cloud data from LiDAR sensors to generate high-definition (HD) maps, which serve as the foundation for localization and mapping [31; 32]. However, these systems face inherent limitations stemming from the characteristics of LiDAR data, such as occlusion issues caused by the linear propagation of signals, low resolution resulting from limited channel density in point clouds, and the absence of color and texture information. To address these challenges, LiDAR data are often fused with camera data, enabling the system to overcome these deficiencies and improve the accuracy of localization and mapping processes [31; 32]. Moreover, the reliability of the global path planning algorithm is highly correlated with the exploration space, which is closely related to the HD map generation range of each vehicle [33]. Therefore, various studies have been conducted to expand the exploration space, which can be broadly categorized into two approaches [34; 35; 36; 37; 38; 39; 40]. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Scenario \#1**} & \multicolumn{3}{c}{**Scenario \#2**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Init. Path Node**} & \multicolumn{2}{c}{**Replan Path Node**} & \multicolumn{2}{c}{**Init. Path Node**} & \multicolumn{2}{c}{**Replan Path Node**} \\ \cline{2-9}  & **Average** & **STD** & **Average** & **STD** & **Average** & **STD** & **Average** & **STD** \\ \hline M1 & 2871.78 & 15.17 & 1870.59 & 15.13 & 1943.23 & 55.42 & 1123.96 & 58.25 \\ \hline M2 & 2334.93 & 31.05 & 1488.06 & 56.91 & 1497.76 & 72.20 & 1021.23 & 64.04 \\ \hline M3 & 1955.42 & 48.63 & 1299.77 & 55.71 & 1559.80 & 92.87 & 920.13 & 69.01 \\ \hline M4 & 2860.22 & 21.71 & 1896.65 & 13.73 & 2535.7 The first approach focuses on expanding the sensor detection range [34; 35]. This method involves using LiDAR, radar, or high-resolution cameras with a wider detection radius to acquire or fuse more refined information from distant areas, thereby enlarging the exploration space. However, in the case of LiDAR, increasing the detection range significantly raises costs, which may reduce its commercial viability. Additionally, even with a wider sensor range, performance limitations may arise due to reduced recognition accuracy at longer distances and the potential for occlusions within the detection range. The second approach involves expanding the exploration space through information exchange between nearby vehicles or infrastructures via networks [36; 37; 38; 39; 40]. In this method, adjacent vehicles or infrastructures collect information from their respective perception sensors and share it with each other.