A human-like trajectory planning method by learning from naturalistic driving data
Trajectory planning has generally been framed as finding the lowest cost one from a set of
trajectory candidates, where the cost function has been hand-crafted with carefully tuned …
trajectory candidates, where the cost function has been hand-crafted with carefully tuned …
Interaction-aware probabilistic behavior prediction in urban environments
Planning for autonomous driving in complex, urban scenarios requires accurate prediction
of the trajectories of surrounding traffic participants. Their future behavior depends on their …
of the trajectories of surrounding traffic participants. Their future behavior depends on their …
End-to-end interactive prediction and planning with optical flow distillation for autonomous driving
With the recent advancement of deep learning technology, data-driven approaches for
autonomous car prediction and planning have achieved extraordinary performance …
autonomous car prediction and planning have achieved extraordinary performance …
Prediction failure risk-aware decision-making for autonomous vehicles on signalized intersections
Motion prediction modules are crucial for autonomous vehicles to forecast the future
behavior of surrounding road users. Failures in prediction modules can mislead a …
behavior of surrounding road users. Failures in prediction modules can mislead a …
Vehicle trajectory prediction based on intention-aware non-autoregressive transformer with multi-attention learning for Internet of Vehicles
X Chen, H Zhang, F Zhao, Y Cai… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
As a core function of autonomous driving (AD) and the Internet of Vehicles (IoV), accurately
predicting the trajectory of vehicles can significantly improve traffic safety and reduce crash …
predicting the trajectory of vehicles can significantly improve traffic safety and reduce crash …
Heterogeneous edge-enhanced graph attention network for multi-agent trajectory prediction
Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential
for the safe and efficient operation of connected automated vehicles under complex driving …
for the safe and efficient operation of connected automated vehicles under complex driving …
Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks
V Kosaraju, A Sadeghian… - Advances in neural …, 2019 - proceedings.neurips.cc
Predicting the future trajectories of multiple interacting pedestrians in a scene has become
an increasingly important problem for many different applications ranging from control of …
an increasingly important problem for many different applications ranging from control of …
Towards capturing the temporal dynamics for trajectory prediction: a coarse-to-fine approach
Trajectory prediction is one of the basic tasks in the autonomous driving field, which aims to
predict the future position of other agents around the ego vehicle so that a safe yet efficient …
predict the future position of other agents around the ego vehicle so that a safe yet efficient …
Dag-net: Double attentive graph neural network for trajectory forecasting
A Monti, A Bertugli, S Calderara… - 2020 25th International …, 2021 - ieeexplore.ieee.org
Understanding human motion behaviour is a critical task for several possible applications
like self-driving cars or social robots, and in general for all those settings where an …
like self-driving cars or social robots, and in general for all those settings where an …
Map-adaptive multimodal trajectory prediction using hierarchical graph neural networks
Predicting the multimodal future motions of neighboring agents is essential for an
autonomous vehicle to navigate complex scenarios. It is challenging as the motion of an …
autonomous vehicle to navigate complex scenarios. It is challenging as the motion of an …