An Online Self-learning Graph-based Lateral Controller for Self-Driving Cars

TL;DR

This paper introduces an online, self-learning, graph-based lateral controller for autonomous vehicles that adapts to changing conditions by learning vehicle models and control policies in real-time using Graph Neural Networks, demonstrated on CARLA.

Contribution

It proposes a novel online learning approach using graph neural networks for vehicle modeling and lateral control, enhancing adaptability in autonomous driving.

Findings

Effective online vehicle model learning with GNNs.

Satisfactory lateral control performance on CARLA platform.

Adaptive control under varying environmental conditions.

Abstract

The hype around self-driving cars has been growing over the past years and has sparked much research. Several modules in self-driving cars are thoroughly investigated to ensure safety, comfort, and efficiency, among which the controller is crucial. The controller module can be categorized into longitudinal and lateral controllers in which the task of the former is to follow the reference velocity, and the latter is to reduce the lateral displacement error from the reference path. Generally, a tuned controller is not sufficient to perform in all environments. Thus, a controller that can adapt to changing conditions is necessary for autonomous driving. Furthermore, these controllers often depend on vehicle models that also need to adapt over time due to varying environments. This paper uses graphs to present novel techniques to learn the vehicle model and the lateral controller online. First, a heterogeneous graph is presented depicting the current states of and inputs to the vehicle. The vehicle model is then learned online using known physical constraints in conjunction with the processing of the graph through a Graph Neural Network structure. Next, another heterogeneous graph - depicting the transition from current to desired states - is processed through another Graph Neural Network structure to generate the steering command on the fly. Finally, the performance of this self-learning model-based lateral controller is evaluated and shown to be satisfactory on an open-source autonomous driving platform called CARLA.