26,338 research outputs found
Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture
In this paper, we propose a deep learning based vehicle trajectory prediction
technique which can generate the future trajectory sequence of surrounding
vehicles in real time. We employ the encoder-decoder architecture which
analyzes the pattern underlying in the past trajectory using the long
short-term memory (LSTM) based encoder and generates the future trajectory
sequence using the LSTM based decoder. This structure produces the most
likely trajectory candidates over occupancy grid map by employing the beam
search technique which keeps the locally best candidates from the decoder
output. The experiments conducted on highway traffic scenarios show that the
prediction accuracy of the proposed method is significantly higher than the
conventional trajectory prediction techniques
EquiDiff: A Conditional Equivariant Diffusion Model For Trajectory Prediction
Accurate trajectory prediction is crucial for the safe and efficient
operation of autonomous vehicles. The growing popularity of deep learning has
led to the development of numerous methods for trajectory prediction. While
deterministic deep learning models have been widely used, deep generative
models have gained popularity as they learn data distributions from training
data and account for trajectory uncertainties. In this study, we propose
EquiDiff, a deep generative model for predicting future vehicle trajectories.
EquiDiff is based on the conditional diffusion model, which generates future
trajectories by incorporating historical information and random Gaussian noise.
The backbone model of EquiDiff is an SO(2)-equivariant transformer that fully
utilizes the geometric properties of location coordinates. In addition, we
employ Recurrent Neural Networks and Graph Attention Networks to extract social
interactions from historical trajectories. To evaluate the performance of
EquiDiff, we conduct extensive experiments on the NGSIM dataset. Our results
demonstrate that EquiDiff outperforms other baseline models in short-term
prediction, but has slightly higher errors for long-term prediction.
Furthermore, we conduct an ablation study to investigate the contribution of
each component of EquiDiff to the prediction accuracy. Additionally, we present
a visualization of the generation process of our diffusion model, providing
insights into the uncertainty of the prediction
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
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
A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction
Accurate and robust trajectory prediction of neighboring agents is critical
for autonomous vehicles traversing in complex scenes. Most methods proposed in
recent years are deep learning-based due to their strength in encoding complex
interactions. However, unplausible predictions are often generated since they
rely heavily on past observations and cannot effectively capture the transient
and contingency interactions from sparse samples. In this paper, we propose a
hierarchical hybrid framework of deep learning (DL) and reinforcement learning
(RL) for multi-agent trajectory prediction, to cope with the challenge of
predicting motions shaped by multi-scale interactions. In the DL stage, the
traffic scene is divided into multiple intermediate-scale heterogenous graphs
based on which Transformer-style GNNs are adopted to encode heterogenous
interactions at intermediate and global levels. In the RL stage, we divide the
traffic scene into local sub-scenes utilizing the key future points predicted
in the DL stage. To emulate the motion planning procedure so as to produce
trajectory predictions, a Transformer-based Proximal Policy Optimization (PPO)
incorporated with a vehicle kinematics model is devised to plan motions under
the dominant influence of microscopic interactions. A multi-objective reward is
designed to balance between agent-centric accuracy and scene-wise
compatibility. Experimental results show that our proposal matches the
state-of-the-arts on the Argoverse forecasting benchmark. It's also revealed by
the visualized results that the hierarchical learning framework captures the
multi-scale interactions and improves the feasibility and compliance of the
predicted trajectories
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