# A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving
## 3 Experiments and Discussions
### Experiments and Results Analysis
#### 3.2.2 Quantitative Evaluation  
During the data collection phase, few turning events were detected at junctions. In order to get more representative results for the evaluation, the proposed approach evaluated non-junction related behaviors, i.e., LK, LCR, and LCL. The proposed approach (referred to in the following as Approach A) was compared with the state-of-art model [12; 16] (Approach B), which is also based on HMMs but selects the most similar behavior whose model holds the maximum likelihood without consideration of background knowledge by Equation (19). Bearing in mind that most events belong to LK, evaluation measures for the imbalance data [38] were selected to evaluate the performance of Approaches A and B, i.e.,  
\[prc=\frac{TPR}{TPR+FPR} \tag{23}\]  
\[F_{1}=\frac{2 prc\cdot TPR}{prc+TPR} \tag{24}\]  
\[G-mean=\sqrt{TPR\cdot TNR} \tag{25}\]  
Figure 10: Collected trajectories in the experimental vehicle’s coordinate system. where TPR, TNR, and the false positive rate (FPR) are calculated by their definitions [38]. Because there are three behaviors for the non-junction road segments, this system belongs to a multi-class classification problem. The measures for each maneuver were calculated against the remaining classes. The final results are shown in Figure 11. The results of Approach A are plotted by blue lines and the results of Approach B are plotted by black lines. Different maneuvers are distinguished by different markers. The results for 0.5 s, 1.0 s, and 1.5 s prediction time horizons are also extracted and presented in Table 4.  
In general, compared with Approach B, the precision for all the behaviors can be improved obviously by the proposed approach, especially for long-term prediction. Even when the prediction time horizon is 1.5 s, our approach still can maintain precision of 68% for the LK maneuver and around 95% for LCL or LCR maneuvers, which are improved by more than 26%. The significant improvement of \(F_{1}\) score and \(G-mean\) also verified the long-term prediction ability of the proposed approach, i.e., the prediction time horizon can be extended significantly. The developed system can predict the target vehicles' future driving behaviors before 2.1 s for LCR and 1.85 s for LCL, which is 56% longer than Approach B. This is mainly because of the combination of a priori probabilities, which implied multiple interactions with surrounding entities based on the interpreting of traffic scenarios. Based on the results, with consideration of a priori knowledge, the target vehicles' driving behaviors can be predicted more quickly and precisely. As the prediction time horizon increases, the performance of Approach B will decrease sharply (Figure 11a). However, the proposed approach can keep a slower decreasing rate. After 1.5 s, all three measurements are below 90%. This is mainly caused by the stochastic behavior of the target vehicle. It is even harder for a human driver to predict surrounding vehicles' behaviors over such a long term. Most previous studies just try to improve the prediction performance of the lane-changing behavior (LCL, LCR), but the precision of LK was neglected [14; 16]. In this study, we found that LK is pretty hard to predict for the urban roadways (the lowest precision in Figure 11). This may be caused by the highly dynamic nature of urban traffic. Compared with freeway traffic, the speed of the target vehicles is lower but more unexpected behaviors occur, like a sharp cut-in.