Name
Abstract | ||
|---|---|---|
Modeling the stochastic behavior of interacting agents is key for safe motion planning. In this paper, we study the interaction of risk-aware agents in a game-theoretical framework. Under the entropic risk measure, we derive an iterative algorithm for approximating the intractable feedback Nash equilibria of a risk-sensitive dynamic game. We use an iteratively linearized approximation of the system dynamics and a quadratic approximation of the cost function in solving a backward recursion for finding feedback Nash equilibria. In this respect, the algorithm shares a similar structure with DDP and iLQR methods. We conduct experiments in a set of challenging scenarios such as roundabouts. Compared to ignoring the game interaction or the risk sensitivity, we show that our risk-sensitive game-theoretic framework leads to more timeefficient, intuitive, and safe behaviors when facing underlying risks and uncertainty. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IROS45743.2020.9341137 | IROS |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingyu Wang | 1 | 135 | 24.90 |
| Negar Mehr | 2 | 4 | 5.52 |
| Adrien Gaidon | 3 | 3 | 1.75 |
| Mac Schwager | 4 | 930 | 72.33 |