# A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving
## 1 Introduction  
Autonomous vehicles (AVs), as one of the most promising topics in both academia and industry in recent decades, are expected to change the future of the world in various ways [1]. AVs can benefit the next generation of transportation systems due to their safety, mobility, and efficiency advantages [2]. Moreover, AVs are effective test platforms for various advanced technologies, such as artificial intelligence, automation control, machine learning, and so on [1]. Based on efforts from around the world, related technologies have been studied in depth [1, 3]. Considerable related applications have been tested in controlled environments and applied in commercial vehicles [3]. For example, Google's AVs have driven more than 1.2 million miles on public roads [4]. All Tesla vehicles in production are equipped with all the hardware needed for fully autonomous capability and newer applications will gradually be activated [5]. Although preliminary successes have been achieved with AVs, there are still many intractable issues from both a technical and a societal perspective [2, 6]. Generally, the architecture of AVs can be divided into three layers, i.e., a perception layer, a planning layer, and a trajectory control layer [1]. The perception layer aggregates real-time data from multiple sensors to estimate the surrounding scenarios. The trajectory control layer generates commands to control the vehicle according to the planned trajectory and velocity. As the core module,planning layer usually consists of three modules, i.e., route planning, behavior decision-making, and path planning. Route and path planning module generate proper global routes and safe local trajectories. The behavior decision-making module provides safe and reasonable abstract driving actions (e.g., \"change lane to left\"). The state-of-the-art decision-making system of AVs should have a forward-looking ability, which means that the surrounding vehicles' future driving behavior should be predicted accurately [7]. Without the prediction function, emergency incidents may happen, such as the collision between MIT's \"Talos\" AV and Cornell's \"Skynet\" AV during the 2007 urban challenge, which was held by the Defense Advanced Research Projects Agency (DAPRA) [3]. As mentioned above, driving behavior prediction plays a significant role in AVs' decision-making system. However, as latent information, the surrounding vehicles' future behaviors cannot be directly measured by any available sensors or obtained by inter-vehicle communication technologies (e.g., Connected Vehicles (CV)-related technologies have not been widely deployed) [8]. In order to predict surrounding vehicles' driving behavior, AVs should monitor the variation in the target vehicle's movement and understand the current scenario from the view of the target vehicle. Then, the future behavior can be predicted by prediction models. Usually, three types of information are applied, i.e., vehicle kinematics, the relationships between target vehicle and surrounding entities (e.g., other vehicles, lane lines), and a priori knowledge. Vehicle kinematics refers to the target vehicle's movement states, such as the lateral velocity and acceleration. The interactions with road elements and other vehicles are described by relative distance, velocity, and direction from the target vehicle to a given entity. These relationships can be characterized by continuous values (e.g., 10 meters) or logical sentences (e.g., \"_isCloseTo_\"). At each time step, for a given target vehicle, a traffic scenario can be defined by these relationships. Each relationship can be considered as one scenario-dependent characteristic. A diversity of scenarios can be defined for the variation of these characteristics. The interactions between the target vehicle and road entities or other vehicles are regulated by a priori knowledge (e.g., traffic rules and commonsense knowledge of driving) [9].