# A Speed Tracking Method for Autonomous Driving via ADRC with Extended State Observer
## 5 Autonomous Driving Vehicle Configuration and Road Experiments Results  
Road tests were conducted on the Tianjin University campus to observe the performance of the MFC-ADRC controller. Figure 11 shows the software architecture of the experimental autonomous vehicle. Sensors used for perception included GPS/IMU, lidar, radar, cameras, and other vehicle sensors. The perception module created a description of the traffic environment for subsequent use by other modules. Other information concerning the traffic environment was provided by a high-definition map (HD-Map) which contained highly detailed topological information of the roadway, such as the locations of lane markers, road edges, traffic lights, and traffic signs to centimeter-level accuracy. Other information, including lane direction, speed limitations, traffic rules governing lane occupancy, and topological information about all the roads and lanes, was stored as formatted data in the HD-Map database. Road incline information needed in by the MFC-ADRC could be easily obtained from the HD-Map.  
Table 6 gives an example of data from the HD-Map; Figure 12 is a representation of the centerline data layer of the HD-Map of Tianjin University. A reference trajectory was generated from the HD-Map data, which were transferred to the decision and planning module. The target trajectory was then calculated by the planning module, and the vehicle control module operated the actuators (brake, throttle, and steering wheel) to follow the trajectory.  
Figure 11: System architecture of Tianjin University’s autonomous vehicle.  
Figure 10: Comparison of engine working point in changing driving style.  
The experimental vehicle was a hybrid SUV which could operate in engine-drive-only mode and which was equipped with the sensors identified at the start of this section. The vehicle could be controlled through wired actuators. The control commands from the vehicle controller were delivered to the wired actuators using the CAN bus. The control software for this autonomous vehicle was implemented on an embedded NVIDIA(r)/XAVIER(tm) GPU platform. A human driver could quickly take over control of all the actuators through a driving mode switch button should safety concerns have demanded it. Figure 13 shows the vehicle hardware setup and the communication paths between modules.  
In the interests of safety, the road tests for the longitudinal controller we developed were carried out on the autonomous driving test field at Tianjin University. Figure 14 gives an overhead view of the test route. There were three speed bumps, a straight-line road, a long curving road, and a roundabout on the test route. Various road conditions were used during the assessment of the longitudinal control algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Lane** & **Pose** & **Width** & **Road ID** & **Num Lane** & **Lateral** & **Next Lane** \\ \hline
104 & 19.42, 15.96 & 3.5 & 21 & 2 & 1 & 106 \\
105 & 18.91, 15.93 & 3.5 & 21 & 2 & 1 & 107 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Example of data from the high definition-map (HD-Map) database.  
Figure 12: Trajectory centerline layer of the HD-Map.  
Figure 13: Hardware setup of Tianjin University’s autonomous vehicle.  
The autonomous driving vehicle had three driving modes: Electric-only, engine-only, and hybrid. We used the engine-only mode to evaluate the performance of the proposed controller because of the nonlinear dynamics of the engine. The results of the experiments are as follows.