NeuroDOB: A Deep Neural Observer-Based Controller for Vehicle Lateral Dynamics

TL;DR

NeuroDOB introduces a deep neural network-based observer controller for vehicle lateral dynamics, enhancing personalized control by learning driver-specific behaviors and unmodeled dynamics, and integrating with LQR for adaptive autonomous driving.

Contribution

The paper presents NeuroDOB, a novel deep neural network observer that replaces traditional disturbance observers, enabling personalized and adaptive vehicle lateral control.

Findings

NeuroDOB outperforms conventional LQR controllers in lateral control tasks.

The DNN effectively learns driver-specific steering compensation signals.

Experimental results demonstrate improved control accuracy and adaptability.

Abstract

This paper proposes NeuroDOB, a deep neural network based observer controller for vehicle lateral dynamics, which replaces the conventional disturbance observer (DOB) with a deep neural network (DNN) to enhance personalized lateral control. Unlike conventional DOBs that compensate for general disturbances such as road friction variation and crosswind, NeuroDOB explicitly addresses unmodeled vehicle dynamics and driver-specific behaviors by learning the steering compensation signal from driver-in-the-loop simulations using CarSim's embedded controller as a surrogate driver. The proposed architecture integrates NeuroDOB with a linear quadratic regulator (LQR), where the DNN outputs a delta error correction added to the baseline LQR steering input to produce the final control command. Input features to the DNN include lateral position and yaw angle errors, and the LQR control input. Experimental validation using a lateral dynamic bicycle model within CarSim demonstrates that NeuroDOB effectively adapts to individual driving habits, improving lateral control performance beyond what conventional LQR controllers achieve. The results indicate the potential of deep neural network based observer to enable personalized and adaptive autonomous vehicle control. In cognitive terms, the proposed architecture can be viewed as a dual-system control structure. The baseline LQR corresponds to System 1, a model-based, fast, and analytic reasoning layer ensuring stability. The NeuroDOB acts as System 2, a reflective, data-driven layer that learns compensation from experience and corrects the analytical bias of System 1. Together, they form an integrated decision process analogous to human intuition-reflection interaction, enabling both stability and adaptability in lateral control.