Accurate Path Tracking by Adjusting Look-Ahead Point in Pure Pursuit Method

Abstract

Path tracking is an essential aspect of the navigational process of self-driving cars. Especially, accurate path tracking is important for not only normal urban roads but also narrow and complex roads such as parking lot and alleyway. The pure pursuit method is one of the geometric path-tracking methods. Using this method, the look-ahead point can be selected far away and the control input is computed in real-time, which is advantageous when the given path is not smooth or when the path is specified using waypoints. Moreover, this method is more robust to localization errors than the model-based path-tracking method. However, the original pure pursuit method and its variants have limited tracking performance. Therefore, this paper proposes a new method that heuristically selects a look-ahead point by considering the relationship between a vehicle and a path. Using this new look-ahead point, the vehicle can stably converge to the desired path and track the path without encountering the cutting-corner problem. The proposed method was tested using simulation and our self-driving car platform. Our results show that the vehicle tracks the desired path more accurately using our proposed algorithm than using the previous pure pursuit methods.