# A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving
## 4 Conclusions  
In this study, a scenario-adaptive driving behavior prediction approach was proposed for autonomous driving on urban roadways. The scenario-dependent characteristics (road elements, traffic participants, and their inter-relationships) were represented by the proposed ontology model. The continuous features of each driving behavior were learned by HMMs with field data. Based on a priori knowledge (traffic rules, driving experience), a rule-based reasoning method is designed to specify candidate models, input features, and a priori probabilities for different scenarios. The future behavior of each target vehicle was predicted with consideration of both the a priori probabilities and the testing results of trained HMMs (a posteriori probabilities).  
Figure 14: The results for Scenario 2. (**a**) Scenario reasoning; (**b**) Likelihood of different behaviors; (**c**) Driving behavior prediction interface.  
Experiments were conducted to evaluate the proposed approach. Based on the experimental results, the driving scenarios on the urban roadways can be described and interpreted by the proposed ontology model. For various urban traffic scenarios, the candidate models, input features, and a priori probabilities can be generated. The target vehicle's lane-changing behaviors can be predicted on average 1.85 s (for LCL) or 2.10 s (for LCR) in advance. Compared with the state-of-the-art driving behavior prediction model, the proposed approach can extend the prediction time horizon by up to 56% (0.76 s) on average, and the precision can be improved by 26% for long-term predictions. The proposed ontology model can improve an autonomous vehicle's scenario understanding ability greatly with the considering of semantic information. The evolution of the scenario can be predicted by the proposed approach, which can serve as the basis for safer decision-making algorithms for AVs.  
Even though the proposed approach has advantages for driving behavior prediction, there are still some limitations, such as the time-consuming and tedious work to edit the SWRL rules. In future work, we will apply more advanced methods to generate these rules, such as learning rules from the training data. Furthermore, more field data will be collected and the proposed framework will be verified for more scenarios (e.g., highway entrance, T-junctions, uncontrolled intersections, etc.) with more driving behaviors.  
The authors gratefully acknowledge the help of our team members, whose contributions were essential for the development of the intelligent vehicle. The authors would also like to thank the Institute of Applied Technology, Hefei Institute of Physical Science, and the Academy of Sciences of China for supporting this study. This work was supported by the National Natural Science Foundation of China under contract nos. 61503362, 91420104, 51405471 and 61503363, the Natural Science Foundation of Anhui Province under contract no. 1508085 MF133.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; and [PERSON] wrote the paper.  
The authors declare no conflict of interest.