Parameter Space and Model Regulation Based Robust, Scalable and Replicable Lateral Control Design for Autonomous Vehicles

This paper introduces a unified, scalable and replicable approach to make implementation of the autonomous system on a new vehicle faster while preserving its autonomous performance. The main idea of this approach is to create a standard hardware architecture, along with a Simulink or similar library and templates for autonomous driving for a unified approach to vehicle autonomy, making it easier to scale the solution and replicate it on other vehicle platforms. However, this scaling and replicating of the autonomous driving system between vehicles remains difficult especially for low-level controller design due to parametric difference between vehicles. This paper, hence, demonstrates a sequential controller design procedure with specific example of lateral control for a chosen vehicle. The same design process can be replicated to adapt controller parameters for other vehicles. The parameter space approach is applied here to ensure robust path following performance of a proportional-derivative (PD) steering controller, considering uncertainties of vehicle load, speed and tire cornering stiffness. To further reduce the tracking error and handle unmodeled dynamics and reject disturbances, a model regulator was added based on overall system analysis. To evaluate the control strategy, a validated high-fidelity model of an autonomous research vehicle is used within a hardware-in-the-loop (HIL) simulation environment. Soft sensors were also connected to the soft automated vehicle in the HIL environment to test high-level control and decision making mechanisms. The road used for the simulations is a replica of a designated real world short AV pilot route in the Ohio State University West campus. Traffic is generated with Simulation of Urban MObility (SUMO) software in order to analyze the problems due to the presence of other vehicles and evaluate performance more realistically in the HIL simulator.