Convention-based Intent Integration in a Haptic Shared Control Paradigm

Abstract

This paper establishes a platform to study the principles of convention formation in a haptic shared control framework wherein both humans and automation collaboratively control the steering of a semi-automated ground vehicle. We then apply these principles to determine optimal strategies for exchanging the control authority between human drivers and an automation system. In the first step, we introduce a modular structure to separate partner-specific conventions from task-dependent representations. Next, we develop a map that connects different forms of conventions to the outputs of human-automation interaction. Finally, using the convention map, we developed a reinforcement-learning-based model predictive controller to enable the automation system to learn complex policies and adapt its behavior accordingly. We applied the proposed platform to the problem of intent negotiation for resolving a conflict. Specifically, we considered a scenario where both humans and automation detect an obstacle but choose different paths to maneuver around the obstacle. The simulation results demonstrate that the convention-based handover strategies can successfully resolve a conflict and improve the performance of the human-automation teaming.