# A Hierarchical Control System for Autonomous Driving towards Urban Challenges
## 2 Hierarchical Control System
### Vehicle Control Strategy
#### 2.3.2 Adaptive-Pure Pursuit Algorithm Based-Lateral Controller
Thus the occupancy grid map starts to populate. The second instance is of a detoured path that is being followed by the vehicle. The result of the local path with hybrid A* in real-world experiments are demonstrated in Rviz in Figure 18. In Figure 18a, when the vehicle enters the obstacle zone where the perception detects. The vehicle follows a straight path until finding a route to pass the obstacles as shown in the Figure 18b. The green path is continuously updated and published to the control until the car reaches the goal point Figure 18c,d. Figure 16: The illustration of local path planning algorithm, (**a**,**b**) are lane-changing processes, (**c**,**d**) are path smoothing. ## 4 Conclusions

In this paper, we address the analysis and design of a hierarchical control system for a self-driving car in the urban environment. Besides, this work also presents the tuning and implementation of the system into practice. The progress addresses the hierarchical control system from the low to high-level control. A raw-trajectory is generated from the global path planning with the aid of an HD map and wireless communication V2X. Firstly, the decision-making mechanism is proposed to handle the missions and control states on the way by implementing the two-stage FSM. Then, the local path

Figure 17: (**a,b**) Clothoid visualizer application; (**c**) occupancy grid map with path generation. Figure 18: The local path with hybrid A* integrated into Rviz, (**a**) begins finding a route, (**b**) found a route, (**c**) following the route, (**d**) finish A* algorithm. planning selects a decision and utilizes it to produce a reference trajectory to the control. Secondly, the local path planning generates an online trajectory with the jerk minimization and the hybrid A* to secure lane-change and avoid obstacles, respectively. The controllers operate the vehicle by commanding throttle, brake, steering angle to follow the local route efficiently with longitudinal and lateral controllers. We implement this work successfully with ROS middleware. The on-going works are the implementation of the autonomous vehicle working robustly and efficiently under all-weather and terrain conditions of the urban environment. Methodology, [PERSON], [PERSON], [PERSON] and [PERSON]; [PERSON], [PERSON] and [PERSON] conducted the experiments and analyzed the results; [PERSON] wrote the original draft; [PERSON]-W.K. reviewed and edited the draft. Supervision, [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript. This research was supported in part by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2018006154) and in part by the MSIT(Ministry of Science, ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2020-2016-0 00465) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation). The authors declare no conflict of interest. ## Appendix A Coordinate Transformation

To implement local path planning and control algorithms, we need to transform between local (vehicle) and global frame (UTM coordinate), as shown in Figure 15.0 The transformation of a local point in vehicle frame \(\{c\}\) to world frame \(\{W\}\) using the homogeneous transform is yielded in Equation (A1). \[\left[ \begin{array}{c}w\\ w\\ y_{w}\\ 1\end{array} \right]=\left[ \begin{array}{cc}w\\ 0\end{array} \right.R_{C}&w_{P_{C}}\\ 0&1\end{array}\right]\left[ \begin{array}{c}c\\ c_{xw}\\ y_{w}\\ 1\end{array} \right]=T\left[ \begin{array}{c}c_{xw}\\ y_{w}\\ 1\end{array} \right]\left(T\in SE(3)\right),\] (A1)

where \({}^{y}\alpha_{x}\) represents the transformation from \(x\) frame to \(y\) frame, \(R\) is rotation matrix. Equation (A1) can be written as

\[\left\{ \begin{array}{c}w\\ x_{w}\\ y_{w}\\ 1\end{array} \right\}=T^{-1}\left[ \begin{array}{c}w\\ w\\ y_{w}\\ 1\end{array} \right]=\left[ \begin{array}{cc}w\\ w\\ 0\end{array} \right.R_{C}&w_{P_{C}}\\ 0&1\end{array}\right]^{-1}\left[ \begin{array}{c}w\\ w\\ y_{w}\\ 1\end{array}