# A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving
## 3 Experiments and Discussions
### Experiments and Results Analysis
#### 3.2.3 Field Test  
The ontology-based traffic scenario representation and rule-based reasoning module were compiled by SWI-Prolog and integrated with the experimental vehicle's main procedure. The learned HMMs for different driving behaviors were loaded into the memory during the initialization process. In this section, two representative experimental scenarios (as Figure 12 shows) are illustrated. The first scenario is at an arterial route, where there is a broken-down car parked in the third lane (counted from the right side of the route). A warning triangle is placed at the rear of the broken car. Some vehicles and motorcycles are rushing from the back of the ego vehicle. The white vehicle numbered 56 (_vehicle56_; the ID was assigned according to the target tracking algorithm) is approaching the warning triangle (_staticobstacle101_), while vehicle 60 (_vehicle60_) and vehicle 63 (_vehicle63_) are moving in the second lane. The driving behavior prediction algorithm can predict all the behaviors for these three vehicles and the motorcycle (_dynamicobstacle57_). In this paper, we just present the results for _vehicle56_, which is surrounded by two right-side moving vehicles and a static obstacle at the front. The second scenario is near a supermarket; there are three moving vehicles (_vehicle346_, _vehicle379_, and _vehicle409_), one junction (_junction214_) with a sidewalk (_sidewalk75_), and one pavement sign at the front of the ego vehicle. Some vehicles are parked in the rightmost lane. _vehicle379_ is maintaining a high speed and the proceeding vehicle has a very low velocity. _vehicle409_ is also keeping a high speed to travel through the sidewalk. This scenario can be considered an overlapped scenario, consisting of a junction scenario and a non-junction scenario. The results of driving behavior prediction for _vehicle379_ are presented in the following paragraphs. For Scenario 1, in fact, _vehicle56_ changed lanes to the right before arriving at the warning triangle. Approach A and Approach B were applied to predict this lane-changing behavior. The results for Scenario 1 are shown in Figure 13. For Approach A, the relative positions of the moving obstacles and their semantic interrelationships (shown in Figure 13a) are analyzed first. Then, the rule-based reasoning module generated the candidate models and input features of the models according to the relationships between _vehicle56_ and the surrounding objects (for _vehicle56_, Rule #2 in Table 2 is activated). The a priori probabilities were combined with the a posteriori probabilities by Equation (20) and the behavior tag is defined by Equation (21). Figure 13b shows the results of Approaches A and B. Approach A can predict the LCR intention about 1.64 s before the RP, while Approach B can predict the LCR intention 0.67s before the RP. This means Approach A can extend the prediction time horizon by 0.95 s. The scenario inference interface of the proposed approach is shown in Figure 13c. The non-drivable area is in red and static obstacles are represented by white dots. The moving obstacles are plotted in red color and their centers (yellow dots) are also marked. The behavior generated by the proposed approach for each target is represented by corresponding abbreviations and probability bars. Figure 12: Two experimental scenarios. (**a**) Scenario 1: a broken-down car stops at an arterial route; (**b**) Scenario 2: a car cuts in from the adjacent lane. The processed results for Scenario 2 are shown in Figure 14. Rule #3 in Table 2 is activated for this scenario. According to the scenario description and reasoning module, there are just two segments (_segment1010_, _segment1011_) connected to _junction214_. The restriction between _segment1010_ and _segment1011_ forbids the _vehicle379_ to TR or TL. So the candidate models for this scenario are limited to LK, SS, LCL, and LCR. Because this is an uncontrolled intersection and there is no pedestrian crossing _sidewalk75_, it can be implied that _vehicle379_ is not intent to stop before the stop line.