## 3 Simulation Analysis
### Simulation Result  
Considering three distinct spatial road characteristics, we factored in vehicle data, road specifics, and obstacle details to determine the control points for the local avoidance algorithm. Following this, we conducted curve discretization interpolation to devise the corresponding local avoidance path. For comparative analysis, we also generated a local avoidance path employing Bezier curves. As can be seen from Figure 5, local avoidance paths are illustrated for three distinct spatial road features. The primary lane of the intelligent driving sightseeing vehicle is marked in black, while neighboring lanes are shown in blue. The paths determined by the conventional Bezier curve are represented by the red line, whereas the B-spline curve paths are delineated by the green line. Across all road variations, both the B-spline and Bezier  
Figure 4: Local obstacle avoidance path. curves adeptly design local avoidance trajectories. It is worth noting that paths from the B-spline curve display a more fluid transition with milder curvature changes. This distinction is especially evident on roads characterized by pronounced curves or continuous bends, underscoring the B-spline curve's enhanced capability for smooth obstacle circumvention. In the path-tracking model, the intelligent driving sightsseeing vehicle navigates two distinct obstacle avoidance paths on roads characterized by varying spatial features, doing so at two separate speeds of 20 km/h and 60 km/h. Utilizing the Carsim simulation platform, real-time metrics for front wheel steering angles and vehicle yaw angles are captured. This data serves as a benchmark to assess the fluidity of the obstacle avoidance paths. Figures 6-8 depict the corresponding front wheel steering angles and vehicle yaw angles for the three specific road conditions. Figure 5: Comparison of local obstacle avoidance paths under three different conditions. As can be seen from Figure 6, real-time variations in the vehicle's front wheel steering angles and yaw angles emerge when traversing a straight road. When following the Bezier curve for obstacle avoidance, the maximum steering angles of the front wheel peak at 2.9\({}^{\circ}\) and 3.5\({}^{\circ}\), with the maximum yaw angle rates of change reaching 6 deg/s and 17 deg/s, respectively. Conversely, when navigating via the refined B-spline curve obstacle avoidance method, the maximum steering angles of the front wheel are limited to 2.9\({}^{\circ}\) and 2.7\({}^{\circ}\), and the peak yaw angle rate of change stands at 5.5 deg/s and 13 deg/s, respectively. Such observations underscore that on straight roads, trajectories designed using the B-spline curve algorithm manifest inherently smoother dynamics. Figure 6: Data comparison under straight road conditions. Figure 7: Data comparison under curved road conditions. As can be seen from Figure 7, there are notable variations in the vehicle's front wheel steering angles and yaw angles on a curved road. When the vehicle follows the Bezier curve obstacle avoidance path, the maximum steering angles of the front wheel measure at \(7^{\circ}\) and \(9.5^{\circ}\), while the maximum yaw angle rate of changes are \(14.8\) deg/s and \(30\) deg/s, respectively. On the other hand, when utilizing the B-spline curve obstacle avoidance method, the steering angles of the front wheel peak at \(6.2^{\circ}\) and \(7^{\circ}\), with the highest yaw angle rates of change being \(12\) deg/s and \(32\) deg/s, respectively. These findings suggest that on curved roads, the intelligent sightseeing vehicle demonstrates a more fluid driving experience when following the trajectory crafted using the B-spline curve algorithm. As can be seen from Figure 8, the vehicle's front wheel steering angle and its heading angle exhibit distinct dynamics under continuous curve road conditions. When adhering to the Bezier curve obstacle avoidance trajectory, the front wheel reaches steering angles of \(43^{\circ}\) and \(78^{\circ}\) at their peak.