73,009 research outputs found
Context-Aware Trajectory Prediction
Human motion and behaviour in crowded spaces is influenced by several
factors, such as the dynamics of other moving agents in the scene, as well as
the static elements that might be perceived as points of attraction or
obstacles. In this work, we present a new model for human trajectory prediction
which is able to take advantage of both human-human and human-space
interactions. The future trajectory of humans, are generated by observing their
past positions and interactions with the surroundings. To this end, we propose
a "context-aware" recurrent neural network LSTM model, which can learn and
predict human motion in crowded spaces such as a sidewalk, a museum or a
shopping mall. We evaluate our model on a public pedestrian datasets, and we
contribute a new challenging dataset that collects videos of humans that
navigate in a (real) crowded space such as a big museum. Results show that our
approach can predict human trajectories better when compared to previous
state-of-the-art forecasting models.Comment: Submitted to BMVC 201
Context-aware Pedestrian Trajectory Prediction with Multimodal Transformer
We propose a novel solution for predicting future trajectories of
pedestrians. Our method uses a multimodal encoder-decoder transformer
architecture, which takes as input both pedestrian locations and ego-vehicle
speeds. Notably, our decoder predicts the entire future trajectory in a
single-pass and does not perform one-step-ahead prediction, which makes the
method effective for embedded edge deployment. We perform detailed experiments
and evaluate our method on two popular datasets, PIE and JAAD. Quantitative
results demonstrate the superiority of our proposed model over the current
state-of-the-art, which consistently achieves the lowest error for 3 time
horizons of 0.5, 1.0 and 1.5 seconds. Moreover, the proposed method is
significantly faster than the state-of-the-art for the two datasets of PIE and
JAAD. Lastly, ablation experiments demonstrate the impact of the key multimodal
configuration of our method
Motion planning in dynamic environments using context-aware human trajectory prediction
Over the years, the separate fields of motion planning, mapping, and human trajectory prediction have advanced considerably. However, the literature is still sparse in providing practical frameworks that enable mobile manipulators to perform whole-body movements and account for the predicted motion of moving obstacles. Previous optimisation-based motion planning approaches that use distance fields have suffered from the high computational cost required to update the environment representation. We demonstrate that GPU-accelerated predicted composite distance fields significantly reduce the computation time compared to calculating distance fields from scratch. We integrate this technique with a complete motion planning and perception framework that accounts for the predicted motion of humans in dynamic environments, enabling reactive and pre-emptive motion planning that incorporates predicted motions. To achieve this, we propose and implement a novel human trajectory prediction method that combines intention recognition with trajectory optimisation-based motion planning. We validate our resultant framework on a real-world Toyota Human Support Robot (HSR) using live RGB-D sensor data from the onboard camera. In addition to providing analysis on a publicly available dataset, we release the Oxford Indoor Human Motion (Oxford-IHM) dataset and demonstrate state-of-the-art performance in human trajectory prediction. The Oxford-IHM dataset is a human trajectory prediction dataset in which people walk between regions of interest in an indoor environment. Both static and robot-mounted RGB-D cameras observe the people while tracked with a motion-capture system
Implicit scene context-aware interactive trajectory prediction for autonomous driving
The accurate prediction of behaviors of surrounding traffic participants is critical for autonomous vehicles (AV). How to fully encode both explicit (e.g., map structure and road geometry) and implicit scene context information (e.g., traffic rules) within complex scenarios is still challenging. In this work, we propose an implicit scene context-aware trajectory prediction framework (the PRISC-Net, Prediction with Implicit Scene Context) for accurate and interactive behavior forecasting. The novelty of the proposed approach includes: 1) development of a behavior prediction framework that takes advantage of both model- and learning-based approaches to fully encode scene context information while modeling complex interactions; 2) development of a candidate path target predictor that utilizes explicit and implicit scene context information for candidate path target prediction, along with a motion planning-based generator that generates kinematic feasible candidate trajectories; 3) integration of the proposed target predictor and trajectory generator with a learning-based evaluator to capture complex agent-agent and agent-scene interactions and output accurate predictions. Experiment results based on vehicle behavior datasets and real-world road tests show that the proposed approaches outperform state-of-the-art methods in terms of prediction accuracy and scene context compliance
Staged Contact-Aware Global Human Motion Forecasting
Scene-aware global human motion forecasting is critical for manifold
applications, including virtual reality, robotics, and sports. The task
combines human trajectory and pose forecasting within the provided scene
context, which represents a significant challenge.
So far, only Mao et al. NeurIPS'22 have addressed scene-aware global motion,
cascading the prediction of future scene contact points and the global motion
estimation. They perform the latter as the end-to-end forecasting of future
trajectories and poses. However, end-to-end contrasts with the coarse-to-fine
nature of the task and it results in lower performance, as we demonstrate here
empirically.
We propose a STAGed contact-aware global human motion forecasting STAG, a
novel three-stage pipeline for predicting global human motion in a 3D
environment. We first consider the scene and the respective human interaction
as contact points. Secondly, we model the human trajectory forecasting within
the scene, predicting the coarse motion of the human body as a whole. The third
and last stage matches a plausible fine human joint motion to complement the
trajectory considering the estimated contacts.
Compared to the state-of-the-art (SoA), STAG achieves a 1.8% and 16.2%
overall improvement in pose and trajectory prediction, respectively, on the
scene-aware GTA-IM dataset. A comprehensive ablation study confirms the
advantages of staged modeling over end-to-end approaches. Furthermore, we
establish the significance of a newly proposed temporal counter called the
"time-to-go", which tells how long it is before reaching scene contact and
endpoints. Notably, STAG showcases its ability to generalize to datasets
lacking a scene and achieves a new state-of-the-art performance on CMU-Mocap,
without leveraging any social cues. Our code is released at:
https://github.com/L-Scofano/STAGComment: 15 pages, 7 figures, BMVC23 ora
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
Exploiting Map Topology Knowledge for Context-predictive Multi-interface Car-to-cloud Communication
While the automotive industry is currently facing a contest among different
communication technologies and paradigms about predominance in the connected
vehicles sector, the diversity of the various application requirements makes it
unlikely that a single technology will be able to fulfill all given demands.
Instead, the joint usage of multiple communication technologies seems to be a
promising candidate that allows benefiting from characteristical strengths
(e.g., using low latency direct communication for safety-related messaging).
Consequently, dynamic network interface selection has become a field of
scientific interest. In this paper, we present a cross-layer approach for
context-aware transmission of vehicular sensor data that exploits mobility
control knowledge for scheduling the transmission time with respect to the
anticipated channel conditions for the corresponding communication technology.
The proposed multi-interface transmission scheme is evaluated in a
comprehensive simulation study, where it is able to achieve significant
improvements in data rate and reliability
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