68,995 research outputs found
Stochastic Sampling Simulation for Pedestrian Trajectory Prediction
Urban environments pose a significant challenge for autonomous vehicles (AVs)
as they must safely navigate while in close proximity to many pedestrians. It
is crucial for the AV to correctly understand and predict the future
trajectories of pedestrians to avoid collision and plan a safe path. Deep
neural networks (DNNs) have shown promising results in accurately predicting
pedestrian trajectories, relying on large amounts of annotated real-world data
to learn pedestrian behavior. However, collecting and annotating these large
real-world pedestrian datasets is costly in both time and labor. This paper
describes a novel method using a stochastic sampling-based simulation to train
DNNs for pedestrian trajectory prediction with social interaction. Our novel
simulation method can generate vast amounts of automatically-annotated,
realistic, and naturalistic synthetic pedestrian trajectories based on small
amounts of real annotation. We then use such synthetic trajectories to train an
off-the-shelf state-of-the-art deep learning approach Social GAN (Generative
Adversarial Network) to perform pedestrian trajectory prediction. Our proposed
architecture, trained only using synthetic trajectories, achieves better
prediction results compared to those trained on human-annotated real-world data
using the same network. Our work demonstrates the effectiveness and potential
of using simulation as a substitution for human annotation efforts to train
high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table
Stochastic Sampling Simulation for Pedestrian Trajectory Prediction
Urban environments pose a significant challenge for autonomous vehicles (AVs)
as they must safely navigate while in close proximity to many pedestrians. It
is crucial for the AV to correctly understand and predict the future
trajectories of pedestrians to avoid collision and plan a safe path. Deep
neural networks (DNNs) have shown promising results in accurately predicting
pedestrian trajectories, relying on large amounts of annotated real-world data
to learn pedestrian behavior. However, collecting and annotating these large
real-world pedestrian datasets is costly in both time and labor. This paper
describes a novel method using a stochastic sampling-based simulation to train
DNNs for pedestrian trajectory prediction with social interaction. Our novel
simulation method can generate vast amounts of automatically-annotated,
realistic, and naturalistic synthetic pedestrian trajectories based on small
amounts of real annotation. We then use such synthetic trajectories to train an
off-the-shelf state-of-the-art deep learning approach Social GAN (Generative
Adversarial Network) to perform pedestrian trajectory prediction. Our proposed
architecture, trained only using synthetic trajectories, achieves better
prediction results compared to those trained on human-annotated real-world data
using the same network. Our work demonstrates the effectiveness and potential
of using simulation as a substitution for human annotation efforts to train
high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table
Multiverse: Mobility pattern understanding improves localization accuracy
Department of Computer Science and EngineeringThis paper presents the design and implementation of Multiverse, a practical indoor localization system that can be deployed on top of already existing WiFi infrastructure. Although the existing WiFi-based positioning techniques achieve acceptable accuracy levels, we find that existing solutions are not practical for use in buildings due to a requirement of installing sophisticated access point (AP) hardware or special application on client devices to aid the system with extra information. Multiverse achieves sub-room precision estimates, while utilizing only received signal strength indication (RSSI) readings available to most of today's buildings through their installed APs, along with the assumption that most users would walk at the normal speed. This level of simplicity would promote ubiquity of indoor localization in the era of smartphones.ope
A novel video-vibration monitoring system for walking pattern identification on floors
This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordWalking-induced loads on office floors can generate unwanted vibrations. The current multiperson loading models are limited since they do not take into account nondeterministic factors
such as pacing rates, walking paths, obstacles in walking paths, busyness of floors, stride lengths,
and interactions among the occupants. This study proposes a novel video-vibration monitoring
system to investigate the complex human walking patterns on floors. The system is capable of
capturing occupant movements on the floor with cameras, and extracting walking trajectories
using image processing techniques. To demonstrate its capabilities, the system was installed on a
real office floor and resulting trajectories were statistically analyzed to identify the actual
walking patterns, paths, pacing rates, and busyness of the floor with respect to time. The
correlation between the vibration levels measured by the wireless sensors and the trajectories
extracted from the video recordings were also investigated. The results showed that the proposed
video-vibration monitoring system has strong potential to be used in training data-driven crowd
models, which can be used in future studies to generate realistic multi-person loading scenarios.Qatar National Research Foundatio
Mountain trail formation and the active walker model
We extend the active walker model to address the formation of paths on
gradients, which have been observed to have a zigzag form. Our extension
includes a new rule which prohibits direct descent or ascent on steep inclines,
simulating aversion to falling. Further augmentation of the model stops walkers
from changing direction very rapidly as that would likely lead to a fall. The
extended model predicts paths with qualitatively similar forms to the observed
trails, but only if the terms suppressing sudden direction changes are
included. The need to include terms into the model that stop rapid direction
change when simulating mountain trails indicates that a similar rule should
also be included in the standard active walker model.Comment: Introduction improved. Analysis of discretization errors added.
Calculations from alternative scheme include
The benefits of using a walking interface to navigate virtual environments
Navigation is the most common interactive task performed in three-dimensional virtual environments (VEs), but it is also a task that users often find difficult. We investigated how body-based information about the translational and rotational components of movement helped participants to perform a navigational search task (finding targets hidden inside boxes in a room-sized space). When participants physically walked around the VE while viewing it on a head-mounted display (HMD), they then performed 90% of trials perfectly, comparable to participants who had performed an equivalent task in the real world during a previous study. By contrast, participants performed less than 50% of trials perfectly if they used a tethered HMD (move by physically turning but pressing a button to translate) or a desktop display (no body-based information). This is the most complex navigational task in which a real-world level of performance has been achieved in a VE. Behavioral data indicates that both translational and rotational body-based information are required to accurately update one's position during navigation, and participants who walked tended to avoid obstacles, even though collision detection was not implemented and feedback not provided. A walking interface would bring immediate benefits to a number of VE applications
Agent Behaviour Simulator (ABS):a platform for urban behaviour development
Computer Graphics have become important for many applicationsand the quality of the produced images have greatly improved. Oneof the interesting remaining problems is the representation of densedynamic environments such as populated cities. Although recentlywe saw some successfulwork on the rendering such environments,the real?time simulation of virtual cities populated by thousands ofintelligent animated agents is still very challenging.In this paperwe describe a platformthat aims to accelerate the developmentof agent behaviours. The platform makes it easy to enterlocal rules and callbacks which govern the individual behaviours.It automatically performs the routine tasks such as collision detectionallowing the user to concentrate on defining the more involvedtasks. The platform is based on a 2D-grid with a four-layered structure.The two first layers are used to compute the collision detectionagainst the environment and other agents and the last two are usedfor more complex behaviours.A set of visualisation tools is incorporated that allows the testingof the real?time simulation. The choices made for the visualisationallow the user to better understand the way agents move inside theworld and how they take decisions, so that the user can evaluate ifit simulates the expected behaviour.Experimentation with the system has shown that behaviours inenvironments with thousands of agents can be developed and visualisedin effortlessly
Improving Livability Using Green and Active Modes: A Traffic Stress Level Analysis of Transit, Bicycle, and Pedestrian Access and Mobility
Understanding the relative attractiveness of alternatives to driving is vitally important toward lowering driving rates and, by extension, vehicle miles traveled (VMT), traffic congestion, greenhouse gas (GHG) emissions, etc. The relative effectiveness of automobile alternatives (i.e., buses, bicycling, and walking) depends on how well streets are designed to work for these respective modes in terms of safety, comfort and cost, which can sometimes pit their relative effectiveness against each other. In this report, the level of traffic stress (LTS) criteria previously developed by two of the authors was used to determine how the streets functioned for these auto alternative modes. The quality and extent of the transit service area was measured using a total travel time metric over the LTS network. The model developed in this study was applied to two transit routes in Oakland, California, and Denver, Colorado
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