MCENET: Multi-context encoder network for homogeneous agent trajectory prediction in mixed traffic
Trajectory prediction in urban mixed-traffic zones (aka shared spaces) is critical for many
intelligent transportation systems, such as intent detection for autonomous driving. However …
intelligent transportation systems, such as intent detection for autonomous driving. However …
FFINet: Future Feedback Interaction Network for Motion Forecasting
Motion forecasting plays a crucial role in autonomous driving, with the aim of predicting the
future reasonable motions of traffic agents. Most existing methods mainly model the …
future reasonable motions of traffic agents. Most existing methods mainly model the …
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 …
Implicit latent variable model for scene-consistent motion forecasting
In order to plan a safe maneuver an autonomous vehicle must accurately perceive its
environment, and understand the interactions among traffic participants. In this paper, we …
environment, and understand the interactions among traffic participants. In this paper, we …
Pip: Planning-informed trajectory prediction for autonomous driving
It is critical to predict the motion of surrounding vehicles for self-driving planning, especially
in a socially compliant and flexible way. However, future prediction is challenging due to the …
in a socially compliant and flexible way. However, future prediction is challenging due to the …
CASPNet++: Joint Multi-Agent Motion Prediction
The prediction of road users' future motion is a critical task in supporting advanced driver-
assistance systems (ADAS). It plays an even more crucial role for autonomous driving (AD) …
assistance systems (ADAS). It plays an even more crucial role for autonomous driving (AD) …
Lanercnn: Distributed representations for graph-centric motion forecasting
Forecasting the future behaviors of dynamic actors is an important task in many robotics
applications such as self-driving. It is extremely challenging as actors have latent intentions …
applications such as self-driving. It is extremely challenging as actors have latent intentions …
KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections
Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic
management and autonomous driving systems. However it presents unique challenges due …
management and autonomous driving systems. However it presents unique challenges due …
Traj-mae: Masked autoencoders for trajectory prediction
Trajectory prediction has been a crucial task in building a reliable autonomous driving
system by anticipating possible dangers. One key issue is to generate consistent trajectory …
system by anticipating possible dangers. One key issue is to generate consistent trajectory …
Hpnet: Dynamic trajectory forecasting with historical prediction attention
Predicting the trajectories of road agents is essential for autonomous driving systems. The
recent mainstream methods follow a static paradigm which predicts the future trajectory by …
recent mainstream methods follow a static paradigm which predicts the future trajectory by …