Human-robot interaction for truck platooning using hierarchical dynamic games

This paper proposes a controller design framework for autonomous truck platoons to ensure safe interaction with a human-driven car. The interaction is modelled as a hierarchical dynamic game, played between the human driver and the nearest truck in the platoon. The hierarchical decomposition is temporal with a high-fidelity tactical horizon predicting immediate interactions and a low-fidelity strategic horizon estimating long-horizon behaviour. The hierarchical approach enables feasible computations where human uncertainties are represented by the quantal response model, and the truck is supposed to maximise its payoff. The closed-loop control is validated via case studies using a driving simulator, where we compare our approach with a short-horizon alternative using only the tactical horizon. The results indicate that our controller is more situation-aware resulting in natural and safe interactions.