# A Driving Behavior Planning and Trajectory Generation Method for Autonomous Electric Bus
## 1 Introduction  
Autonomous driving technology improves active traffic safety and brings convenience to human beings. It has received much attention from researchers and scholars. Compared with unmanned cars, autonomous busses drive on relatively fixed routes, at relatively slow speeds, and for shorter distances in urban areas. At the same time, the role of bus drivers is transformed into a bus safety officer to decrease manpower fatigue. Thus, autonomous busses are more likely to come about earlier than driverless cars in everyday life. However, driving behavior planning and trajectory generation are still a challenging problem for autonomous vehicle technology. During the global planning stage, global routes are determined by a high-precision digital map and a localization system [1]. The global planning methods include the artificial potential field (APF) [2], cell decomposition [3], visibility graph [4], Voronoi diagram [5], and probabilistic road maps [6]. All these methods establish the model of the environmental map by grid or nodes and then compute the weights to search the optimal path. They are time consuming, especially in complex environments. Many scholars focus on sampling-based algorithms for on-line path planning such as the probabilistic roadmap method (PRM) [7] and the rapidly-exploring random tree (RRT) [8]. Unfortunately, these methods produce solutions that are suboptimal or even far from optimal and the resulting path is not continuous. Nowadays, more and more researchers begin to apply intelligence computation to solve this problem. For instance, [9] proposes a global path planning of wheeled robots by multi-objective memetic algorithms (MOMAs). A modified particle swarm optimization (PSO) algorithm is utilized to obtain a high-quality global path for a mobile robot in [10]. An intelligence algorithm could find the global optimal solution, but it needs more computation time. Local planning contains two parts, driving behavior planning, and trajectory generation. Driving behavior of the autonomous bus is determined by global planning and perception information [11]. For example, [12] introduces a driving decision mechanism based on decision tree, and the multiple criteria decision making is utilized in [13]. Reference [14] presents an integrated behavioral inference and decision-making, it simulates vehicle behavior for both autonomous vehicle and nearby vehicles. Meanwhile, [15] proposes a human-like decision-making algorithm to address decision making problem in dynamic, uncertain scenarios such as the simple intersections and roundabouts. However, these methods often explore unlikely regions of the belief space and increase the calculation time. Finite state machine (FSM) is expressed by the sequences of driving behavior [16; 17], it is a practical discrete sample method which is usually applied to driverless vehicle system. However, it is more suitable for specific and simplified situations and not robust to a varied world. Generally, trajectory planning is an essential and key part of robotics motion planning. Nowadays, many researchers usually rest on some simple assumptions about the vehicle kinematics. The trajectory is expressed by a variety of methods, including polynomial [18], Bezel curve [19], Spline curve [20], and so on. A quaternary fourth order polynomial and the dynamic bicycle model are adopted in [6] to describe the vehicle motion equation. Reference [21] uses a family of parameterized trajectories, which contains geometric constraints and performance index. These methods are usually converted into constrained optimization, which increase the complexity greatly. Extensive studies have been conducted on autonomous vehicles recently, but few have studied the technology of autonomous busses. In this paper, we mainly propose a framework that is applied to an autonomous electric bus successfully. For global planning, we apply ArcGIS to model a road network and plan a global optimal path.