| 1. |
- Fisac, Jaime F., et al.
(author)
-
Hierarchical Game-Theoretic Planning for Autonomous Vehicles
- 2019
-
In: 2019 International Conference on Robotics and Automation, (ICRA). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538660263 ; , s. 9590-9596
-
Conference paper (peer-reviewed)abstract
- The actions of an autonomous vehicle on the road affect and are affected by those of other drivers, whether overtaking, negotiating a merge, or avoiding an accident. This mutual dependence, best captured by dynamic game theory, creates a strong coupling between the vehicle's planning and its predictions of other drivers' behavior, and constitutes an open problem with direct implications on the safety and viability of autonomous driving technology. Unfortunately, dynamic games are too computationally demanding to meet the real-time constraints of autonomous driving in its continuous state and action space. In this paper, we introduce a novel game-theoretic trajectory planning algorithm for autonomous driving, that enables real-time performance by hierarchically decomposing the underlying dynamic game into a long-horizon "strategic" game with simplified dynamics and full information structure, and a short-horizon "tactical" game with full dynamics and a simplified information structure. The value of the strategic game is used to guide the tactical planning, implicitly extending the planning horizon, pushing the local trajectory optimization closer to global solutions, and, most importantly, quantitatively accounting for the autonomous vehicle and the human driver's ability and incentives to influence each other. In addition, our approach admits non-deterministic models of human decision-making, rather than relying on perfectly rational predictions. Our results showcase richer, safer, and more effective autonomous behavior in comparison to existing techniques.
|
|
| 2. |
- Stefansson, Elis, et al.
(author)
-
Human-robot interaction for truck platooning using hierarchical dynamic games
- 2019
-
In: Proceedings 2019 18TH EUROPEAN CONTROL CONFERENCE (ECC). - : IEEE. ; , s. 3165-3172
-
Conference paper (peer-reviewed)abstract
- 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.
|
|
