# A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving
## 1 Introduction
For example, it can be believed that a vehicle moving in the leftmost lane has a very low probability of turning left, as otherwise it will crash into the curb. Without an inborn ability to perceive and interpret traffic scenarios accurately predicting driving behavior for typical urban roadways is quite challenging for AVs [10]. Most existing models for driving behavior prediction were based on one or multiple types of information mentioned in the previous paragraph. Table 1 presents related up-to-date studies [11; 12; 13; 14; 15; 16; 17; 18; 19; 20] that predict surrounding vehicles' driving behaviors. In the table, existing related studies are categorized into three groups according to the features applied in the study. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Involved Features** & **Scenario Adaptable** & **Used Methods 2** & **Implemented in** \\ \hline \multirow{3}{*}{Vehicle kinematics, Relations with road entities, Interactions with other vehicles,} & \multirow{3}{*}{no} & TTx & [12; 16] \\  & & HMM & [12; 16] \\  & & SVM-BF & [12; 20] \\ \hline \multirow{3}{*}{Vehicle kinematics, Relations with road entities, Interactions with other vehicles} & \multirow{3}{*}{no} & BN & [11] \\  & & Layered-HMM & [15] \\ \hline \multirow{3}{*}{Vehicle kinematics, Relations with road entities, Interactions with other vehicles, A priori knowledge} & no & DBN & [13] \\  & no & Cost-map + BF & [14] \\ \cline{1-1}  & Yes & HSS-HMM & [17; 18] \\ \cline{1-1}  & Yes & SMT + SLP & [19] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative review of works on driver behavior prediction for surrounding vehicles 1. Vehicle kinematics and relations with road entities were considered by almost all existing studies. These features can be applied directly by TTx (Time to something) models to predict the behavior of the target vehicle. For example, [PERSON] et al. used TTC (Time to Line Crossing) to predict whether the vehicle will depart from the current lane or not [16]. These features can also be pre-learned by models and then we can find the most similar behavior during the on-line prediction phase by pattern matching. The Hidden Markov Model (HMM) and Support Vector Machines (SVM) with Bayesian Filter (BF) are usually applied to learn the pattern of each behavior [12; 20]. Besides vehicle kinematics and relations with road entities, interactions between the target vehicle and its surrounding vehicles also have a significant influence on target vehicles' future behavior. A Dynamic Bayesian Network (DBN) was applied widely to develop interaction-aware prediction models. The interaction-aware models were also believed to be the most comprehensive according to the study of [PERSON] et al. [9]. However, their applications in AVs are limited because of the expensive computation [9]. In order to relieve the curse of dimensionality, [PERSON] et al. [14] proposed a long-term behavior prediction framework in which model-based and supervised-learning-based subtasks were combined. A complex spatial-temporal cost map with consideration of traffic rules was constructed to determine the interactions. However, in this study, just 60 lane changes of other traffic participants were considered and it is difficult to construct a cost map. All the previous studies tried to build a driving behavior prediction approach with consideration of as many clues as possible, but most previous models fail to consider all the clues. In particular, a priori knowledge is never even considered. Most mentioned models were designed for one specific scenario, such as lane-changing models for a non-junction segment, turn left/right models for a junction area, etc. However, AVs drive through dynamically changing environments in which a diversity of scenarios show up over time. Multiple scenario-specific models should be used and appropriate models should be activated according to the scenario characteristics, which means that a reasonable adaptation approach should be designed. Aiming to solve the adaptation issue, [PERSON] et al.