2,190 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions
To plan safe trajectories in urban environments, autonomous vehicles must be
able to quickly assess the future intentions of dynamic agents. Pedestrians are
particularly challenging to model, as their motion patterns are often uncertain
and/or unknown a priori. This paper presents a novel changepoint detection and
clustering algorithm that, when coupled with offline unsupervised learning of a
Gaussian process mixture model (DPGP), enables quick detection of changes in
intent and online learning of motion patterns not seen in prior training data.
The resulting long-term movement predictions demonstrate improved accuracy
relative to offline learning alone, in terms of both intent and trajectory
prediction. By embedding these predictions within a chance-constrained motion
planner, trajectories which are probabilistically safe to pedestrian motions
can be identified in real-time. Hardware experiments demonstrate that this
approach can accurately predict pedestrian motion patterns from onboard
sensor/perception data and facilitate robust navigation within a dynamic
environment.Comment: Submitted to 2014 International Workshop on the Algorithmic
Foundations of Robotic
Transformer Networks for Trajectory Forecasting
Most recent successes on forecasting the people motion are based on LSTM
models and all most recent progress has been achieved by modelling the social
interaction among people and the people interaction with the scene. We question
the use of the LSTM models and propose the novel use of Transformer Networks
for trajectory forecasting. This is a fundamental switch from the sequential
step-by-step processing of LSTMs to the only-attention-based memory mechanisms
of Transformers. In particular, we consider both the original Transformer
Network (TF) and the larger Bidirectional Transformer (BERT), state-of-the-art
on all natural language processing tasks. Our proposed Transformers predict the
trajectories of the individual people in the scene. These are "simple" model
because each person is modelled separately without any complex human-human nor
scene interaction terms. In particular, the TF model without bells and whistles
yields the best score on the largest and most challenging trajectory
forecasting benchmark of TrajNet. Additionally, its extension which predicts
multiple plausible future trajectories performs on par with more engineered
techniques on the 5 datasets of ETH + UCY. Finally, we show that Transformers
may deal with missing observations, as it may be the case with real sensor
data. Code is available at https://github.com/FGiuliari/Trajectory-Transformer.Comment: 18 pages, 3 figure
Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty
Progress towards advanced systems for assisted and autonomous driving is
leveraging recent advances in recognition and segmentation methods. Yet, we are
still facing challenges in bringing reliable driving to inner cities, as those
are composed of highly dynamic scenes observed from a moving platform at
considerable speeds. Anticipation becomes a key element in order to react
timely and prevent accidents. In this paper we argue that it is necessary to
predict at least 1 second and we thus propose a new model that jointly predicts
ego motion and people trajectories over such large time horizons. We pay
particular attention to modeling the uncertainty of our estimates arising from
the non-deterministic nature of natural traffic scenes. Our experimental
results show that it is indeed possible to predict people trajectories at the
desired time horizons and that our uncertainty estimates are informative of the
prediction error. We also show that both sequence modeling of trajectories as
well as our novel method of long term odometry prediction are essential for
best performance.Comment: CVPR 201
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
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