# A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving
## 1 Introduction
proposed a Hybrid State System and Hidden Markov Model (HSS + HMM) to graft and prune \"meta-states\" (a group of candidate models) [17; 18]. The different scenarios were specified by Finite State Machine (FSM) with scenario-dependent features. However, this model was applied for driving behavior estimation (estimating behavior after it has actually happened) and not for prediction. The FSM will become untraceable for large-scale scenarios due to its inherent disadvantages. Another study [19] proposed a hierarchy scenario model tree (SMT) to specify different scenarios and used single-layer perceptron (SLP) to learn scenario-specified behaviors. For both of these related studies, the traffic rules and drivers' driving experience were not considered and so it is difficult to integrate them into their proposed models, such as \"vehicles on a smaller road yield to vehicles on the main road\". Furthermore, simply scenario representation methods were applied and only the relationships between two vehicles were considered. However, the target vehicles' future behavior is influenced by multitudinous surrounding entities. Based on the literature review and the comparison of existing methods, the main objects of this study are summarized as:  
* Improving the adaptability of existing models. In the real world, AVs should drive through a diversity of complex urban scenarios. A reliable activation strategy that can select appropriate candidate prediction models for a given scenario will extend the application scope of the traditional prediction models. * Combining priori knowledge to improve the quality of prediction. Priori knowledge information is helpful for driving behavior prediction [9]. For example, it is believed that a moving vehicle has quite a low probability of changing lanes to the right when there is another vehicle abreast in the right lane. Based on the literature review, it is still not possible to describe these relationships clearly without increasing the computation complexity. Aiming at the aforementioned goals, this study proposed a scenario-adaptive approach to predict driving behaviors on urban roadways. The main contributions of this study are as follows:  
* A novel ontology-based model was proposed to represent the traffic scenarios. This study developed a novel ontology-based model to represent the spatial state of surrounding entities and their inter-relationships. Based on this model, the scenario can be expressed explicitly and semantic inter-relationships can be represented, which makes it more understandable than traditional scenario description approaches. * A rule-based reasoning system was developed to improve the adaptability of the driving behavior prediction models. Based on the structured description of traffic scenarios, adaptation rules were edited. In the rules, candidate scenario-specific models, input features, and priori probabilities for candidate models were specified based on the scenario characteristics. * The future behavior of the target vehicle was determined by both a priori and a posteriori knowledge. This is the first study to combine a priori probability and a posteriori probability for behavior prediction. HMM-based probabilistic models were trained off-line and tested on-line to get the a posteriori probability for each possible behavior. A priori probability is provided by a rule-based reasoning system. In the following sections, the problem formulation and an overview of the proposed approach are presented. The ontology model to describe traffic scenarios and the HMMs to learn continuous features are presented. Then, the approach to predict the future behavior of a target vehicle is given. Finally, the proposed approach is evaluated in real traffic scenarios on urban roadways. The results of the experiments are presented and discussed. We finally conclude this paper with contributions, limitations, and future work in Section 4.