# A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving
## Materials and Methods
### Ontology-Based Scenario Description and Reasoning  
#### 2.2.1 A Novel Ontological Model for Scenario Description  
The traffic scenario can be represented by various methodologies, e.g., an occupancy grid map [7], Bayesian Network [8; 11; 13], etc. The representation methods that just consider the entities' spatial locations (like an occupancy grid map) cannot describe the relationships between different entities. Although a Bayesian Network can depict the inter-relations by carefully designed networks, the network usually will become pretty complex when considering a large number of entities. Moreover, it is hard to express the semantic relationships and integrate traffic rules. With the ability to define hierarchical classes and relationships between these classes, ontologies have been used to represent the contextual entities and expert knowledge unambiguously in knowledge-intensive systems [21; 22]. As an interoperable and understandable expression form, ontology was defined as a formal and explicit specification of a shared conceptualization [23]. For different applications, ontology models should be defined based on corresponding domain knowledge. For autonomous driving, this technology has already been applied in some simple traffic scenarios [21; 24; 25; 26; 27]. However, there is still no standard ontology for autonomous driving on urban roadways so far. In this study, we chose Description Logic (DL) as a formalism to represent the ontology knowledge base and store it with Web Ontology Language 2 (OWL 2) [28]. The DL knowledge base is composed of a terminological box (TBOX) and assertional box (ABOX). TBOX defines concepts (also known as classes) to describe the terminology in the application domain, e.g., _Vehicle_, _Sidewalk_. Each concept is defined by its data property axioms (also named attributes), e.g., _distanceToVehicle_ is equal to 100 m. Relationships between these classes are described by object property axioms, e.g., _Vehicle isCloseTo Sidewalk_. Usually, the defined classes can be structured into a taxonomic topology. In order to describe the hierarchical relations, TBOX defined class expression axioms. These axioms were described by a set of operators such as equivalence operator \(\equiv\), inclusion operator \(\sqsubseteq\), intersection \(\sqcap\), union \(\sqcup\), complement \(\
eg\), etc. Complex descriptions are constructed based on atomic (i.e., basic) concepts \((A,B)\) and atomic roles \(R\). For example, the union of the basic classes or the association between concepts and roles can lead to more complex descriptions, e.g.,  
\[C\equiv A\sqcup B\quad\text{or}\quad D\equiv\exists R.C. \tag{7}\]  
The above classes and axioms are usually defined by domain experts based on their domain knowledge. ABOX defines the instances of TBOX, which are also named individuals \((a,b,...)\). In addition, concept assertions \(B(a)\) and role assertions \(R(a,b)\) were defined to characterize these individuals' properties and relations, e.g., _Vehicle_ (vehicle001), _hasLane_ (segment001, lane012). Figure 1: Overview of the proposed driving behavior prediction approach. In order to represent the complex traffic scenario characteristics and intricate relationships between entities, this study proposed a novel ontology model to explicitly describe the traffic scenarios. The ontology model was edited by Protege [29] software and parts of the ontology terminology are graphically represented in Figure 2. The classes (including _Entity_ branch, _Attributes_ branch) are enclosed by rectangles. Their semantic relationships are connected by the hierarchical object properties (dotted lines of different colors). The taxonomy relationships between concepts are plotted by light blue solid lines. The proposed ontology aims to cover all the possible scenarios in the urban environment. The _Entity_ consists of four sub-entities:  
1. _MapEntity._ The map entity is the most important part to describe the geometric topology of the roadway and road entities.