4,986 research outputs found
Contact Surface Area: A Novel Signal for Heart Rate Estimation in Smartphone Videos
We consider the problem of smartphone video-based heart rate estimation,
which typically relies on measuring the green color intensity of the user's
skin. We describe a novel signal in fingertip videos used for smartphone-based
heart rate estimation: fingertip contact surface area. We propose a model
relating contact surface area to pressure, and validate it on a dataset of 786
videos from 62 participants by demonstrating a statistical correlation between
contact surface area and green color intensity. We estimate heart rate on our
dataset with two algorithms, a baseline using the green signal only and a novel
algorithm based on both color and area. We demonstrate lower rates of
substantial errors (>10 beats per minute) using the novel algorithm (4.1%),
compared both to the baseline algorithm (6.4%) and to published results using
commercial color-based applications (>6%)
Inferring Occluded Agent Behavior in Dynamic Games with Noise-Corrupted Observations
Robots and autonomous vehicles must rely on sensor observations, e.g., from
lidars and cameras, to comprehend their environment and provide safe, efficient
services. In multi-agent scenarios, they must additionally account for other
agents' intrinsic motivations, which ultimately determine the observed and
future behaviors. Dynamic game theory provides a theoretical framework for
modeling the behavior of agents with different objectives who interact with
each other over time. Previous works employing dynamic game theory often
overlook occluded agents, which can lead to risky navigation decisions. To
tackle this issue, this paper presents an inverse dynamic game technique which
optimizes the game model itself to infer unobserved, occluded agents' behavior
that best explains the observations of visible agents. Our framework
concurrently predicts agents' future behavior based on the reconstructed game
model. Furthermore, we introduce and apply a novel receding horizon planning
pipeline in several simulated scenarios. Results demonstrate that our approach
offers 1) robust estimation of agents' objectives and 2) precise trajectory
predictions for both visible and occluded agents from observations of only
visible agents. Experimental findings also indicate that our planning pipeline
leads to safer navigation decisions compared to existing baseline methods
Game-theoretic Occlusion-Aware Motion Planning: an Efficient Hybrid-Information Approach
We present a novel algorithm for motion planning in complex, multi-agent
scenarios in which occlusions prevent all agents from seeing one another. In
this setting, the fundamental information that each agent has, i.e., the
information structure of the interaction, is determined by the precise
configurations in which agents come into view of one another. Occlusions
prevent the use of existing pure feedback solutions, which assume availability
of the state information of all agents at every time step. On the other hand,
existing open-loop solutions only assume availability of the initial agent
states. Thus, they do not fully utilize the information available to agents
during periods of unhampered visibility. Here, we first introduce an algorithm
for solving an occluded, linear-quadratic (LQ) dynamic game, which computes
Nash equilibrium by using hybrid information and switching between feedback and
open-loop information structures. We then design an efficient iterative
algorithm for decision-making which exploits this hybrid information structure.
Our method is demonstrated in overtaking and intersection traffic scenarios.
Results confirm that our method outputs trajectories with favorable running
times, converging much faster than recent methods employing reachability
analysis
Leadership Inference for Multi-Agent Interactions
Effectively predicting intent and behavior requires inferring leadership in
multi-agent interactions. Dynamic games provide an expressive theoretical
framework for modeling these interactions. Employing this framework, we propose
a novel method to infer the leader in a two-agent game by observing the agents'
behavior in complex, long-horizon interactions. We make two contributions.
First, we introduce an iterative algorithm that solves dynamic two-agent
Stackelberg games with nonlinear dynamics and nonquadratic costs, and
demonstrate that it consistently converges. Second, we propose the Stackelberg
Leadership Filter (SLF), an online method for identifying the leading agent in
interactive scenarios based on observations of the game interactions. We
validate the leadership filter's efficacy on simulated driving scenarios to
demonstrate that the SLF can draw conclusions about leadership that match
right-of-way expectations.Comment: 8 pages, 5 figures, submitted for publication to IEEE Robotics and
Automation Letter
GTP-SLAM: Game-Theoretic Priors for Simultaneous Localization and Mapping in Multi-Agent Scenarios
Robots operating in complex, multi-player settings must simultaneously model
the environment and the behavior of human or robotic agents who share that
environment. Environmental modeling is often approached using Simultaneous
Localization and Mapping (SLAM) techniques; however, SLAM algorithms usually
neglect multi-player interactions. In contrast, a recent branch of the motion
planning literature uses dynamic game theory to explicitly model noncooperative
interactions of multiple agents in a known environment with perfect
localization. In this work, we fuse ideas from these disparate communities to
solve SLAM problems with game theoretic priors. We present GTP-SLAM, a novel,
iterative best response-based SLAM algorithm that accurately performs state
localization and map reconstruction in an uncharted scene, while capturing the
inherent game-theoretic interactions among multiple agents in that scene. By
formulating the underlying SLAM problem as a potential game, we inherit a
strong convergence guarantee. Empirical results indicate that, when deployed in
a realistic traffic simulation, our approach performs localization and mapping
more accurately than a standard bundle adjustment algorithm across a wide range
of noise levels.Comment: 6 pages, 3 figure
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