## 4 On-Vehicle Experiment  
The intelligent sightseeing car deployed in our real-world experiments originates from Liuzhou Wuling Automobile Industry Corporation Limited. This particular vehicle boasts a robust and dependable wire-controlled chassis, seamlessly integrating steering, throttle, and braking controls, all managed via signals from its autonomous driving domain controller. The embedded autonomous driving software system of the vehicle is multifaceted, encompassing a high-precision map module, a localization module, a perception module, a decision-making module, a planning module, and a control module. Ensuring efficient inter-module communication, these elements utilize the ROS communication protocol and are meticulously crafted using the C++ programming language. The aesthetics and design of this advanced sightseeing vehicle are showcased in Figure 9.  
Figure 8: Data comparison under continuous curve road conditions.  
The planning module of the intelligent sightseeing car initiates its process by leveraging a lane-following map for global path planning, which delineates the vehicle's overarching route. This module then makes judicious decisions based on real-time spatial perception of obstacles and the associated lane occupation flag, denoted as \"\(i\)\". This flag aids in determining whether the vehicle should navigate to the left or right side of an obstacle. Specifically, the values of \"\(i\)\" can be 0, 1, or 2, which correspond to the middle lane, left lane, and right lane, respectively. By employing the local obstacle avoidance algorithm, the module crafts a local obstacle avoidance path attuned to the prevailing road circumstances. Figure 10 elucidates the algorithmic procedure underpinning this planning module.  
Figure 10: Algorithm flowchart of the planning module.  
Figure 9: Physical image of the intelligent autonomous sightseeing vehicle.  
Experiments using the real vehicle were conducted on a closed two-way lane within the campus. This lane is characterized by both straight and curved sections. Given the constraints of the straight section's length in the test area and in the interest of safety, the vehicle's driving speed was capped at 12 km/h. The lane itself spans a width of 3.5 m, and with the inclusion of curbing on each side, the effective width extends to 4.0 m. In addressing the varied spatial features of the road, the planning module employed both the improved B-spline curve and the Bezier curve for generating obstacle avoidance trajectories. Comprehensive results from these real-world tests are depicted in Figures 11 and 12. These figures present the coordinates of the obstacle avoidance path points and relevant data, including front wheel angles, yaw angles, and yaw rates for the various road conditions encountered.  
As can be seen from Figures 11a and 12a, both algorithms present similar trajectories up until the point they encounter obstacles. Upon confronting these obstacles, both methodologies successfully devise logical obstacle avoidance paths. Nevertheless, the trajectory produced by the B-spline curve demonstrates a marked improvement in smoothness compared to that of the Bezier curve. Delving deeper into Figures 11b-d and 12b-d, it becomes evident that the B-spline curve fitting algorithm leads to fewer fluctuations in both the front wheel angle and the yaw angle. This implies a more consistent and stable performance by  
Figure 11: Data comparison of real vehicle under straight road conditions.  
Figure 12: Data comparison of real vehicle under curved road conditions.  
the intelligent sightseeing vehicle when navigating around obstacles, regardless of whether the road conditions are straight or curved.