48 research outputs found
A Fast and Map-Free Model for Trajectory Prediction in Traffics
To handle the two shortcomings of existing methods, (i)nearly all models rely
on high-definition (HD) maps, yet the map information is not always available
in real traffic scenes and HD map-building is expensive and time-consuming and
(ii) existing models usually focus on improving prediction accuracy at the
expense of reducing computing efficiency, yet the efficiency is crucial for
various real applications, this paper proposes an efficient trajectory
prediction model that is not dependent on traffic maps. The core idea of our
model is encoding single-agent's spatial-temporal information in the first
stage and exploring multi-agents' spatial-temporal interactions in the second
stage. By comprehensively utilizing attention mechanism, LSTM, graph
convolution network and temporal transformer in the two stages, our model is
able to learn rich dynamic and interaction information of all agents. Our model
achieves the highest performance when comparing with existing map-free methods
and also exceeds most map-based state-of-the-art methods on the Argoverse
dataset. In addition, our model also exhibits a faster inference speed than the
baseline methods.Comment: 7 pages, 3 figure
STF: Spatial Temporal Fusion for Trajectory Prediction
Trajectory prediction is a challenging task that aims to predict the future
trajectory of vehicles or pedestrians over a short time horizon based on their
historical positions. The main reason is that the trajectory is a kind of
complex data, including spatial and temporal information, which is crucial for
accurate prediction. Intuitively, the more information the model can capture,
the more precise the future trajectory can be predicted. However, previous
works based on deep learning methods processed spatial and temporal information
separately, leading to inadequate spatial information capture, which means they
failed to capture the complete spatial information. Therefore, it is of
significance to capture information more fully and effectively on vehicle
interactions. In this study, we introduced an integrated 3D graph that
incorporates both spatial and temporal edges. Based on this, we proposed the
integrated 3D graph, which considers the cross-time interaction information. In
specific, we design a Spatial-Temporal Fusion (STF) model including Multi-layer
perceptions (MLP) and Graph Attention (GAT) to capture the spatial and temporal
information historical trajectories simultaneously on the 3D graph. Our
experiment on the ApolloScape Trajectory Datasets shows that the proposed STF
outperforms several baseline methods, especially on the long-time-horizon
trajectory prediction.Comment: 6 pages, 6 figure
Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling
Trajectory generation and trajectory prediction are two critical tasks for
autonomous vehicles, which generate various trajectories during development and
predict the trajectories of surrounding vehicles during operation,
respectively. However, despite significant advances in improving their
performance, it remains a challenging problem to ensure that the
generated/predicted trajectories are realistic, explainable, and physically
feasible. Existing model-based methods provide explainable results, but are
constrained by predefined model structures, limiting their capabilities to
address complex scenarios. Conversely, existing deep learning-based methods
have shown great promise in learning various traffic scenarios and improving
overall performance, but they often act as opaque black boxes and lack
explainability. In this work, we integrate kinematic knowledge with neural
stochastic differential equations (SDE) and develop a variational autoencoder
based on a novel latent kinematics-aware SDE (LK-SDE) to generate vehicle
motions. Our approach combines the advantages of both model-based and deep
learning-based techniques. Experimental results demonstrate that our method
significantly outperforms baseline approaches in producing realistic,
physically-feasible, and precisely-controllable vehicle trajectories,
benefiting both generation and prediction tasks.Comment: 7 pages, conference paper in motion generatio
Scaling Planning for Automated Driving using Simplistic Synthetic Data
We challenge the perceived consensus that the application of deep learning to
solve the automated driving planning task requires a huge amount of real-world
data or a realistic simulator. Using a roundabout scenario, we show that this
requirement can be relaxed in favour of targeted, simplistic simulated data. A
benefit is that such data can be easily generated for critical scenarios that
are typically underrepresented in realistic datasets. By applying vanilla
behavioural cloning almost exclusively to lightweight simulated data, we
achieve reliable and comfortable real-world driving. Our key insight lies in an
incremental development approach that includes regular in-vehicle testing to
identify sim-to-real gaps, targeted data augmentation, and training scenario
variations. In addition to the methodology, we offer practical guidelines for
deploying such a policy within a real-world vehicle, along with insights of the
resulting qualitative driving behaviour. This approach serves as a blueprint
for many automated driving use cases, providing valuable insights for future
research and helping develop efficient and effective solutions
Addressing the Impact of Localized Training Data in Graph Neural Networks
Graph Neural Networks (GNNs) have achieved notable success in learning from
graph-structured data, owing to their ability to capture intricate dependencies
and relationships between nodes. They excel in various applications, including
semi-supervised node classification, link prediction, and graph generation.
However, it is important to acknowledge that the majority of state-of-the-art
GNN models are built upon the assumption of an in-distribution setting, which
hinders their performance on real-world graphs with dynamic structures. In this
article, we aim to assess the impact of training GNNs on localized subsets of
the graph. Such restricted training data may lead to a model that performs well
in the specific region it was trained on but fails to generalize and make
accurate predictions for the entire graph. In the context of graph-based
semi-supervised learning (SSL), resource constraints often lead to scenarios
where the dataset is large, but only a portion of it can be labeled, affecting
the model's performance. This limitation affects tasks like anomaly detection
or spam detection when labeling processes are biased or influenced by human
subjectivity. To tackle the challenges posed by localized training data, we
approach the problem as an out-of-distribution (OOD) data issue by by aligning
the distributions between the training data, which represents a small portion
of labeled data, and the graph inference process that involves making
predictions for the entire graph. We propose a regularization method to
minimize distributional discrepancies between localized training data and graph
inference, improving model performance on OOD data. Extensive tests on popular
GNN models show significant performance improvement on three citation GNN
benchmark datasets. The regularization approach effectively enhances model
adaptation and generalization, overcoming challenges posed by OOD data.Comment: 6 pages, 4 figure
MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networks
Edge intelligence is an emerging paradigm for real-time training and
inference at the wireless edge, thus enabling mission-critical applications.
Accordingly, base stations (BSs) and edge servers (ESs) need to be densely
deployed, leading to huge deployment and operation costs, in particular the
energy costs. In this article, we propose a new framework called
Mobility-Enhanced Edge inTelligence (MEET), which exploits the sensing,
communication, computing, and self-powering capabilities of intelligent
connected vehicles for the smart and green 6G networks. Specifically, the
operators can incorporate infrastructural vehicles as movable BSs or ESs, and
schedule them in a more flexible way to align with the communication and
computation traffic fluctuations. Meanwhile, the remaining compute resources of
opportunistic vehicles are exploited for edge training and inference, where
mobility can further enhance edge intelligence by bringing more compute
resources, communication opportunities, and diverse data. In this way, the
deployment and operation costs are spread over the vastly available vehicles,
so that the edge intelligence is realized cost-effectively and sustainably.
Furthermore, these vehicles can be either powered by renewable energy to reduce
carbon emissions, or charged more flexibly during off-peak hours to cut
electricity bills.Comment: This paper has been accepted by IEEE Communications Magazin
Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric
Heterogeneous graphs offer powerful data representations for traffic, given
their ability to model the complex interaction effects among a varying number
of traffic participants and the underlying road infrastructure. With the recent
advent of graph neural networks (GNNs) as the accompanying deep learning
framework, the graph structure can be efficiently leveraged for various machine
learning applications such as trajectory prediction. As a first of its kind,
our proposed Python framework offers an easy-to-use and fully customizable data
processing pipeline to extract standardized graph datasets from traffic
scenarios. Providing a platform for GNN-based autonomous driving research, it
improves comparability between approaches and allows researchers to focus on
model implementation instead of dataset curation.Comment: Presented at IV 202
RMP: A Random Mask Pretrain Framework for Motion Prediction
As the pretraining technique is growing in popularity, little work has been
done on pretrained learning-based motion prediction methods in autonomous
driving. In this paper, we propose a framework to formalize the pretraining
task for trajectory prediction of traffic participants. Within our framework,
inspired by the random masked model in natural language processing (NLP) and
computer vision (CV), objects' positions at random timesteps are masked and
then filled in by the learned neural network (NN). By changing the mask
profile, our framework can easily switch among a range of motion-related tasks.
We show that our proposed pretraining framework is able to deal with noisy
inputs and improves the motion prediction accuracy and miss rate, especially
for objects occluded over time by evaluating it on Argoverse and NuScenes
datasets.Comment: IEEE International Conference on Intelligent Transportation Systems
(ITSC 2023