Probabilistic multi-modal trajectory prediction with lane attention for autonomous vehicles
Trajectory prediction is crucial for autonomous vehicles. The planning system not only needs
to know the current state of the surrounding objects but also their possible states in the …
to know the current state of the surrounding objects but also their possible states in the …
Lane-attention: Predicting vehicles' moving trajectories by learning their attention over lanes
Accurately forecasting the future movements of surrounding vehicles is essential for safe
and efficient operations of autonomous driving cars. This task is difficult because a vehicle's …
and efficient operations of autonomous driving cars. This task is difficult because a vehicle's …
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 …
Online vehicle trajectory prediction using policy anticipation network and optimization-based context reasoning
In this paper, we present an online two-level vehicle trajectory prediction framework for
urban autonomous driving where there are complex contextual factors, such as lane …
urban autonomous driving where there are complex contextual factors, such as lane …
Ltp: Lane-based trajectory prediction for autonomous driving
The reasonable trajectory prediction of surrounding traffic participants is crucial for
autonomous driving. Especially, how to predict multiple plausible trajectories is still a …
autonomous driving. Especially, how to predict multiple plausible trajectories is still a …
Hierarchical latent structure for multi-modal vehicle trajectory forecasting
D Choi, KW Min - European Conference on Computer Vision, 2022 - Springer
Variational autoencoder (VAE) has widely been utilized for modeling data distributions
because it is theoretically elegant, easy to train, and has nice manifold representations …
because it is theoretically elegant, easy to train, and has nice manifold representations …
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 …
Divide-and-conquer for lane-aware diverse trajectory prediction
Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute
actions. Our work addresses two key challenges in trajectory prediction, learning multimodal …
actions. Our work addresses two key challenges in trajectory prediction, learning multimodal …
Motioncnn: A strong baseline for motion prediction in autonomous driving
S Konev, K Brodt, A Sanakoyeu - arXiv preprint arXiv:2206.02163, 2022 - arxiv.org
To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of
other agents around it. Motion prediction is an extremely challenging task that recently …
other agents around it. Motion prediction is an extremely challenging task that recently …
Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation
Predicting the trajectories of surrounding agents is an essential ability for autonomous
vehicles navigating through complex traffic scenes. The future trajectories of agents can be …
vehicles navigating through complex traffic scenes. The future trajectories of agents can be …