# A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving
## 3 Experiments and Discussions
### Experiments and Results Analysis  
#### 3.2.1 Data Collection and Model Training  
After the construction of the high-resolution map and the knowledge base, field data were collected on the test urban roadways over 15 days. The moving obstacles on the road were tracked at a frequency of 12.5 Hz. The data were recorded into the log files. In the end there were about 1000 events (one event is referred to as one-time tracking of a target vehicle), and 345 target vehicles were acquired. These events are summarized in the right-hand part of Table 3. The collected trajectories (in the experimental autonomous vehicle's coordinate system) for target vehicles are presented in Figure 10. The experimental autonomous vehicle has a 750 \(\times\) 500 rectangular Cartesian coordinate system, in which the heading of the experimental vehicle is the positive direction of the vertical axis and the left direction is the positive direction of the horizontal axis. The size of each cell is 20 cm \(\times\) 20 cm and the coordinates of the experimental vehicle are (250, 500). Because few events were detected at the junctions, all the events corresponding to junction-related maneuvers (i.e., TR, TL, and GS) are grouped together.  
For each event, selected features were identified and extracted. In order to evaluate the performance of the behavior prediction models, the Reference Points (RPs) should be defined to represent the moments that the target vehicle executes maneuvers. For the lane-changing behavior, we  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Road Elements**} & \multicolumn{3}{c}{**Moving Targets**} \\ \hline
**Elements** & **Number** & **Behaviors** & **Events** & **Samples** \\ \hline Route parts & 40 & LK & 412 & 107,272 \\ Segments & 231 & LCL & 198 & 60,701 \\ Lane & 312 & LCR & 289 & 83,289 \\ Markers & 336 & & & \\ Sidewalk & 150 & Junction related & 42 & 4300 \\ Junctions & 40 & & & \\ Traffic Signs & 486 & Others & 313 & 12,421 \\ \hline Total & 1595 & Total & 1,254 & 267,983 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Extracted road elements and collected events.  
Figure 9: Testing roadways with various layouts.  
select the RPs as the moments when the bumper of the target vehicle touches the lane line. Also, any observations from less than 3.2 s before the RPs were considered in this study. Because it is difficult to define RPs for lane keeping events, all the LK samples were considered. Each sample, together with the previous \(O_{w}\) samples, was considered as one LK event.  
The parameters for the HMMs should be selected carefully, i.e., the observation window size \(O_{w}\), the number of the hidden states \(N\), and the number of the mixtures \(M\). In this study, the Monte Carlo testing method was used to test a large set of these parameters' combinations. Each combination was generated with a number between 2 and 20 for \(N\), a number between 5 and 40 for \(O_{w}\), and a number between 2 and 10 for \(M\). For the imbalance of the training data (most events belong to lane keeping), the best combination of HMMs were defined based on the \(G-mean\) value, which was computed by  
\[G-mean=\sqrt[3]{TPR_{LK}\cdot TPR_{LCR}\cdot TPR_{LCL}}, \tag{22}\]  
where TPR and TNR are the true positive rate and the true negative rate, respectively. The parameter combination holding the highest \(G-mean\) value was selected for the final HMM.  
Based on the aforementioned steps, the parameters for the lane-changing behavior models are \(N=6\), \(O_{w}=10\), \(M=2\) with a \(G-mean\) of 0.985.