Path Tracking for Automated Driving: A Tutorial on Control System Formulations and Ongoing Research

In the United States, the DARPA (Defense Advanced Research Projects Agency) Grand Challenges [Thrun et al. (J. Field Robot. 23(9):661–692, 2006); Urmson et al. (J. Field Robot. 25(8):425–466, 2008); Urmson et al. (J. Field Robot. 23(8):467–508, 2006); Campbell (Steering Control of an Autonomous Ground Vehicle with Application to the DARPA Urban Challenge. Massachusetts Institute of Technology, 2007)] demonstrated that autonomous driving can be achieved through vision and sensor systems capable of detecting and interpreting the vehicle operating environment, rather than through autonomous driving options relying on the infrastructure (e.g., through magnets installed on the road surface to indicate the lanes), or vehicle-to-vehicle or vehicle-to-infrastructure communication systems. The latter options are very useful to further enhance the performance, safety, and energy efficiency, but are not strictly required. In a typical automated driving system, a reference path and a reference speed profile are defined based on the sensed environment. At a lower level of the control system hierarchy, a path tracking controller is responsible for calculating the steering angle for achieving the reference trajectory, while a speed controller determines the wheel torque demand for tracking the reference speed. Speed control implementations are already quite common in production vehicles equipped with cruise control and adaptive cruise control systems. Hence, the core element of novelty for autonomous driving in the area of vehicle control is represented by the steering control function for path tracking. Different steering-based path tracking algorithms, ranging from geometrical methods to model-predictive controllers, are presented and discussed in this contribution, together with the expected future research and vehicle implementation directions in the field.