Decoder fusion rnn: Context and interaction aware decoders for trajectory prediction
Forecasting the future behavior of all traffic agents in the vicinity is a key task to achieve safe
and reliable autonomous driving systems. It is a challenging problem as agents adjust their …
and reliable autonomous driving systems. It is a challenging problem as agents adjust their …
Hierarchical motion encoder-decoder network for trajectory forecasting
Q Xue, S Li, X Li, J Zhao, W Zhang - arXiv preprint arXiv:2111.13324, 2021 - arxiv.org
Trajectory forecasting plays a pivotal role in the field of intelligent vehicles or social robots.
Recent works focus on modeling spatial social impacts or temporal motion attentions, but …
Recent works focus on modeling spatial social impacts or temporal motion attentions, but …
Recup net: Recursive prediction network for surrounding vehicle trajectory prediction with future trajectory feedback
In order to predict the behavior of human drivers accurately, the autonomous vehicle should
be able to understand the reasoning and decision process of motion generation of human …
be able to understand the reasoning and decision process of motion generation of human …
Trajectory prediction with graph-based dual-scale context fusion
Motion prediction for traffic participants is essential for a safe and robust automated driving
system, especially in cluttered urban environments. However, it is highly challenging due to …
system, especially in cluttered urban environments. However, it is highly challenging due to …
Exploring dynamic context for multi-path trajectory prediction
To accurately predict future positions of different agents in traffic scenarios is crucial for
safely deploying intelligent autonomous systems in the real-world environment. However, it …
safely deploying intelligent autonomous systems in the real-world environment. However, it …
Multiple trajectory prediction with deep temporal and spatial convolutional neural networks
Automated vehicles need to not only perceive their environment, but also predict the
possible future behavior of all detected traffic participants in order to safely navigate in …
possible future behavior of all detected traffic participants in order to safely navigate in …
Dynamic scenario representation learning for motion forecasting with heterogeneous graph convolutional recurrent networks
Due to the complex and changing interactions in dynamic scenarios, motion forecasting is a
challenging problem in autonomous driving. Most existing works exploit static road graphs to …
challenging problem in autonomous driving. Most existing works exploit static road graphs to …
Exploring map-based features for efficient attention-based vehicle motion prediction
C Gómez-Huélamo, MV Conde, M Ortiz - arXiv preprint arXiv:2205.13071, 2022 - arxiv.org
Motion prediction (MP) of multiple agents is a crucial task in arbitrarily complex
environments, from social robots to self-driving cars. Current approaches tackle this problem …
environments, from social robots to self-driving cars. Current approaches tackle this problem …
Multi-modal motion prediction with transformer-based neural network for autonomous driving
Predicting the behaviors of other agents on the road is critical for autonomous driving to
ensure safety and efficiency. However, the challenging part is how to represent the social …
ensure safety and efficiency. However, the challenging part is how to represent the social …
Query-centric trajectory prediction
Predicting the future trajectories of surrounding agents is essential for autonomous vehicles
to operate safely. This paper presents QCNet, a modeling framework toward pushing the …
to operate safely. This paper presents QCNet, a modeling framework toward pushing the …