Reactive navigation of autonomous vehicle

Research on dynamic path planning for autonomous vehicle has been increased significantly in recent years because of the percentage of car accidents reduced by autonomous driving. The functions of Artificial Potential Fields (APF) as mentioned in previous studies, particularly nonlinear constraint, speed and safe distance, are seldom discussed or mentioned. In this paper, we focus on the need for vehicle safety. Thus, this study proposes a fuzzy artificial potential field path planning algorithm (FZ-APF) for local path planning in vehicle. FZ-APF introduces the distance factor to solve the well-known drawbacks of the traditional APF like goal non-reachable with obstacle nearby (GNRON) problem. However, FZ-APF not only optimizes path but also deals with the constraints of vehicle like the limit of steering angle at different speeds. Therefore, FZ-APF introduces longitudinal potential function to make the planned path accord with vehicle characteristics. In addition, to achieve more safe and efficient FZ-APF for vehicle, the fuzzy technique which adjusts the parameters at any moment according to the current speed and safety distance of the vehicle is added into FZ-APF. Numerical simulations are presented and validate the proposed algorithm under some complicated test scenarios. Experiment eventually illustrates the efficiency of this FZ-APF algorithm using an autonomous vehicle prototype.