Transformer based trajectory prediction
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 which recently …
other agents around it. Motion prediction is an extremely challenging task which recently …
A survey on deep-learning approaches for vehicle trajectory prediction in autonomous driving
With the rapid development of machine learning, autonomous driving has become a hot
issue, making urgent demands for more intelligent perception and planning systems. Self …
issue, making urgent demands for more intelligent perception and planning systems. Self …
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 …
Interacting vehicle trajectory prediction with convolutional recurrent neural networks
S Mukherjee, S Wang, A Wallace - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
Anticipating the future trajectories of surrounding vehicles is a crucial and challenging task
in path planning for autonomy. We propose a novel Convolutional Long Short Term Memory …
in path planning for autonomy. We propose a novel Convolutional Long Short Term Memory …
Transformer networks for trajectory forecasting
Most recent successes on forecasting the people motion are based on LSTM models and all
most recent progress has been achieved by modelling the social interaction among people …
most recent progress has been achieved by modelling the social interaction among people …
Tenet: Transformer encoding network for effective temporal flow on motion prediction
This technical report presents an effective method for motion prediction in autonomous
driving. We develop a Transformer-based method for input encoding and trajectory …
driving. We develop a Transformer-based method for input encoding and trajectory …
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 …
Trajectory prediction based on planning method considering collision risk
Anticipating the trajectory of Autonomous Vehicles (AV) plays an important role in improving
its driving safety. With the rapid development of learning-based method in recent years, the …
its driving safety. With the rapid development of learning-based method in recent years, the …
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving
As an essential task in autonomous driving (AD), motion prediction aims to predict the future
states of surround objects for navigation. One natural solution is to estimate the position of …
states of surround objects for navigation. One natural solution is to estimate the position of …
Conditional generative neural system for probabilistic trajectory prediction
J Li, H Ma, M Tomizuka - 2019 IEEE/RSJ International …, 2019 - ieeexplore.ieee.org
Effective understanding of the environment and accurate trajectory prediction of surrounding
dynamic obstacles are critical for intelligent systems such as autonomous vehicles and …
dynamic obstacles are critical for intelligent systems such as autonomous vehicles and …