Gradient Aware - Shrinking Domain based Control Design for Reactive Planning Frameworks used in Autonomous Vehicles

Abstract

In this paper, we present a novel control law for longitudinal speed control of autonomous vehicles. The key contributions of the proposed work include the design of a control law that reactively integrates the longitudinal surface gradient of road into its operation. In contrast to the existing works, we found that integrating the path gradient into the control framework improves the speed tracking efficacy. Since the control law is implemented over a shrinking domain scheme, it minimizes the integrated error by recomputing the control inputs at every discretized step and consequently provides less reaction time. This makes our control law suitable for motion planning frameworks that are operating at high frequencies. Furthermore, our work is implemented using a generalized vehicle model and can be easily extended to other classes of vehicles. The performance of gradient awareshrinking domain based controller is implemented and tested on a stock electric vehicle on which a number of sensors are mounted. Results from the tests show the robustness of our control law for speed tracking on a terrain with varying gradient while also considering stringent time constraints imposed by the planning framework.

Model Predictive Control for Autonomous Driving considering Actuator Dynamics

“…Finally, a first order model has only one parameter and thus require simpler system identification as compared to a second order actuator dynamics. [15]. Predominantly, the observed response was similar to a first order system.…”

“…The commanded velocity is shown in black, the response in red and the fitted first order model in blue. (a) and (b) show results from our autonomous vehicle hardware a Mahindra E20[15]. Predominantly, the observed response was similar to a first order system.…”

“…which accounts for the inherent relationship between v(t i ) and v c (t i ). First order system: We conducted several experiments with our autonomous vehicle hardware [15] and a high-fidelity simulator CARSIM [16] to analyze the response to a step velocity input. Some sample results are presented in Fig.…”