289 research outputs found
Multi-Hypothesis Interactions in Game-Theoretic Motion Planning
We present a novel method for handling uncertainty about the intentions of
non-ego players in dynamic games, with application to motion planning for
autonomous vehicles. Equilibria in these games explicitly account for
interaction among other agents in the environment, such as drivers and
pedestrians. Our method models the uncertainty about the intention of other
agents by constructing multiple hypotheses about the objectives and constraints
of other agents in the scene. For each candidate hypothesis, we associate a
Bernoulli random variable representing the probability of that hypothesis,
which may or may not be independent of the probability of other hypotheses. We
leverage constraint asymmetries and feedback information patterns to
incorporate the probabilities of hypotheses in a natural way. Specifically,
increasing the probability associated with a given hypothesis from to
shifts the responsibility of collision avoidance from the hypothesized agent to
the ego agent. This method allows the generation of interactive trajectories
for the ego agent, where the level of assertiveness or caution that the ego
exhibits is directly related to the easy-to-model uncertainty it maintains
about the scene.Comment: For associated mp4 file, see https://youtu.be/x7VtYDrWTW
The Computation of Approximate Generalized Feedback Nash Equilibria
We present the concept of a Generalized Feedback Nash Equilibrium (GFNE) in
dynamic games, extending the Feedback Nash Equilibrium concept to games in
which players are subject to state and input constraints. We formalize
necessary and sufficient conditions for (local) GFNE solutions at the
trajectory level, which enable the development of efficient numerical methods
for their computation. Specifically, we propose a Newton-style method for
finding game trajectories which satisfy the necessary conditions, which can
then be checked against the sufficiency conditions. We show that the evaluation
of the necessary conditions in general requires computing a series of nested,
implicitly-defined derivatives, which quickly becomes intractable. To this end,
we introduce an approximation to the necessary conditions which is amenable to
efficient evaluation, and in turn, computation of solutions. We term the
solutions to the approximate necessary conditions Generalized Feedback Quasi
Nash Equilibria (GFQNE), and we introduce numerical methods for their
computation. In particular, we develop a Sequential Linear-Quadratic Game
approach, in which a locally approximate LQ game is solved at each iteration.
The development of this method relies on the ability to compute a GFNE to
inequality- and equality-constrained LQ games, and therefore specific methods
for the solution of these special cases are developed in detail. We demonstrate
the effectiveness of the proposed solution approach on a dynamic game arising
in an autonomous driving application
2011-2012 Master Class - Phillip Evans (Piano)
https://spiral.lynn.edu/conservatory_masterclasses/1065/thumbnail.jp
2010-2011 Master Class - Jonathan Plowright (Piano)
https://spiral.lynn.edu/conservatory_masterclasses/1078/thumbnail.jp
2012-2013 Master Class - Leonard Hindell (Bassoon)
https://spiral.lynn.edu/conservatory_masterclasses/1056/thumbnail.jp
- …