## 6 Simulation Experiments Analysis  
In Figure 13, an optimal route (green line) from the start point to the end point is obtained by the network analysis tool. Except for the general road, there are some special points in Figure 14.  
By reading SP1 and SP2 data, the unique index is worked out, and the special route database is searched to get a special route named the Curve Path. When the car arrives at SP1, the Curve Path (red line) take the place of the green line as its current path. Also, when it arrives at SP2, it continues to use the green line as its path until it drives to the destination in Figure 14.  
We implement the local path planning algorithm with regard to obstacles to avoid the driverless car feasible path in the structured urban environment. Firstly, static/dynamic obstacles perception and lane line detection are significant for drive behavior decisions for local path planning. In addition, the driverless car completes overtaking, unexpected obstacles avoidance, and parking. The simulation experiments results using local path planning based on JPS are as shown in Figure 15.  
Figure 15a shows the experiment scene under the structured urban environment with static obstacles along the road by laser rangefinder. The black solid lines in Figure 15 represent lane lines, and the car is hard to cross, while black dash lines represents the passable lane line. The car could  
\begin{table}
\begin{tabular}{p{227.6 pt}} \hline Step1: Drive behavior decisions, meanwhile, detection of collision car includes two parts, one is detection from vehicle position to control point, the other is detection from control point to adjusted return point; \\ \hline Step2: Structure of searching boundary and avoidance points in the structured urban environment; \\ \hline Step3: Path generation with JPS in Section 4.3; \\ \hline Step4: Trajectory smoothing with Bezier curves in Section 5.4. \\ \hline \end{tabular}
\end{table}
Table 1: Local Path Planning based on JPS in Structured Environment.  
Figure 14: Search and get the special Curve Path.  
achieve lane changing and overtaking in Figure 15b. Figure 15c-e describes behaviors of lane changing and overtaking in the case of collision car (purple triangle) detection at the same lane. The driverless car generates a feasible trajectory to follow the local path planning as a red solid line in Figure 15. If it cannot satisfy lane changing and overtaking conditions, then the driverless car performances are shown in Figure 15f. Thus, it is verified that the proposed path planning method is effective.  
In this paper, we compare five kinds of search algorithms, including A*, JPS, PRM, VFH, and RRT. Meanwhile, experiment condition is the same as in Figure 15b. Results are shown in Table 2, and the comparison results in scene 1 are shown in Figure 16. Five different scenes of each algorithm are tested for time complexity; each scene is run five times for each algorithm in matlab to obtain an average. Therefore, the elapsed time of PRM is the longest, and the elapsed time of JPS is the shortest,  
Figure 15: Path planning results for driverless car in structured urban environment.  
which verifies that the JPS algorithm has the best performance of time cost among these popular algorithms. Meanwhile, the planning paths of A*, JPS, PRM, VFH, and RRT are different due to the different algorithm searching ability, which is another expression of time complexity.