20,131 research outputs found
Hierarchical reasoning game theory based approach for evaluation and testing of autonomous vehicle control systems
A hierarchical game theoretic decision making framework is exploited to model driver decisions and interactions in traffic. In this paper, we apply this framework to develop a simulator to evaluate various existing autonomous driving algorithms. Specifically, two algorithms, based on Stackelberg policies and decision trees, are quantitatively compared in a traffic scenario where all the human-driven vehicles are modeled using the presented game theoretic approach. © 2016 IEEE
GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving
Autonomous vehicles operating in complex real-world environments require
accurate predictions of interactive behaviors between traffic participants.
While existing works focus on modeling agent interactions based on their past
trajectories, their future interactions are often ignored. This paper addresses
the interaction prediction problem by formulating it with hierarchical game
theory and proposing the GameFormer framework to implement it. Specifically, we
present a novel Transformer decoder structure that uses the prediction results
from the previous level together with the common environment background to
iteratively refine the interaction process. Moreover, we propose a learning
process that regulates an agent's behavior at the current level to respond to
other agents' behaviors from the last level. Through experiments on a
large-scale real-world driving dataset, we demonstrate that our model can
achieve state-of-the-art prediction accuracy on the interaction prediction
task. We also validate the model's capability to jointly reason about the ego
agent's motion plans and other agents' behaviors in both open-loop and
closed-loop planning tests, outperforming a variety of baseline methods
Solution Concepts in Hierarchical Games under Bounded Rationality with Applications to Autonomous Driving
With autonomous vehicles (AV) set to integrate further into regular human
traffic, there is an increasing consensus of treating AV motion planning as a
multi-agent problem. However, the traditional game theoretic assumption of
complete rationality is too strong for the purpose of human driving, and there
is a need for understanding human driving as a \emph{bounded rational} activity
through a behavioral game theoretic lens. To that end, we adapt three
metamodels of bounded rational behavior; two based on Quantal level-k and one
based on Nash equilibrium with quantal errors. We formalize the different
solution concepts that can be applied in the context of hierarchical games, a
framework used in multi-agent motion planning, for the purpose of creating game
theoretic models of driving behavior. Furthermore, based on a contributed
dataset of human driving at a busy urban intersection with a total of ~4k
agents and ~44k decision points, we evaluate the behavior models on the basis
of model fit to naturalistic data, as well as their predictive capacity. Our
results suggest that among the behavior models evaluated, modeling driving
behavior as pure strategy NE with quantal errors at the level of maneuvers with
bounds sampling of actions at the level of trajectories provides the best fit
to naturalistic driving behavior, and there is a significant impact of
situational factors on the performance of behavior models
A Review of Platforms for the Development of Agent Systems
Agent-based computing is an active field of research with the goal of
building autonomous software of hardware entities. This task is often
facilitated by the use of dedicated, specialized frameworks. For almost thirty
years, many such agent platforms have been developed. Meanwhile, some of them
have been abandoned, others continue their development and new platforms are
released. This paper presents a up-to-date review of the existing agent
platforms and also a historical perspective of this domain. It aims to serve as
a reference point for people interested in developing agent systems. This work
details the main characteristics of the included agent platforms, together with
links to specific projects where they have been used. It distinguishes between
the active platforms and those no longer under development or with unclear
status. It also classifies the agent platforms as general purpose ones, free or
commercial, and specialized ones, which can be used for particular types of
applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
- …