Neural network modeling for steering control of an autonomous vehicle

Abstract

Model-based control of dynamical systems typically requires accurate domain-specific knowledge and specifications of possibly proprietary system components. In the context of autonomous driving, steering actuator dynamics can be difficult to model due to an integrated proprietary power steering control module. While first-principles models derived from physics laws can often approximate the system behavior, it remains generally difficult to capture non-physically derived behavior based on proprietary software algorithms in the power steering system. To overcome this limitation, this work instead employs a recurring neural network to model the steering dynamics of an autonomous vehicle. The resulting model is then integrated into a Nonlinear Model Predictive Control scheme to generate feedforward steering commands for embedded control. The proposed approach is compared to traditional first-principles steering modeling through on-vehicle experiments and statistical data validation. As a result, it is shown that the neural network model can be automatically generated with less domain-specific knowledge, can predict steering dynamics more accurately, and perform comparably to a high-fidelity first principles model when used for controlling the steering system of a self-driving vehicle.