Macformer: Map-agent coupled transformer for real-time and robust trajectory prediction
C Feng, H Zhou, H Lin, Z Zhang, Z Xu… - IEEE Robotics and …, 2023 - ieeexplore.ieee.org
Predicting the future behavior of agents is a fundamental task in autonomous vehicle
domains. Accurate prediction relies on comprehending the surrounding map, which …
domains. Accurate prediction relies on comprehending the surrounding map, which …
Hivt: Hierarchical vector transformer for multi-agent motion prediction
Accurately predicting the future motions of surrounding traffic agents is critical for the safety
of autonomous vehicles. Recently, vectorized approaches have dominated the motion …
of autonomous vehicles. Recently, vectorized approaches have dominated the motion …
Diverse and admissible trajectory forecasting through multimodal context understanding
Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately
anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable …
anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable …
Multiple trajectory prediction of moving agents with memory augmented networks
Pedestrians and drivers are expected to safely navigate complex urban environments along
with several non cooperating agents. Autonomous vehicles will soon replicate this …
with several non cooperating agents. Autonomous vehicles will soon replicate this …
Multiple futures prediction
C Tang, RR Salakhutdinov - Advances in neural information …, 2019 - proceedings.neurips.cc
Temporal prediction is critical for making intelligent and robust decisions in complex
dynamic environments. Motion prediction needs to model the inherently uncertain future …
dynamic environments. Motion prediction needs to model the inherently uncertain future …
Mtr++: Multi-agent motion prediction with symmetric scene modeling and guided intention querying
Motion prediction is crucial for autonomous driving systems to understand complex driving
scenarios and make informed decisions. However, this task is challenging due to the diverse …
scenarios and make informed decisions. However, this task is challenging due to the diverse …
Learning to predict vehicle trajectories with model-based planning
Predicting the future trajectories of on-road vehicles is critical for autonomous driving. In this
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …
Lapred: Lane-aware prediction of multi-modal future trajectories of dynamic agents
In this paper, we address the problem of predicting the future motion of a dynamic agent
(called a target agent) given its current and past states as well as the information on its …
(called a target agent) given its current and past states as well as the information on its …
Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction
Predicting the future behavior of road users is one of the most challenging and important
problems in autonomous driving. Applying deep learning to this problem requires fusing …
problems in autonomous driving. Applying deep learning to this problem requires fusing …
Amenet: Attentive maps encoder network for trajectory prediction
Trajectory prediction is critical for applications of planning safe future movements and
remains challenging even for the next few seconds in urban mixed traffic. How an agent …
remains challenging even for the next few seconds in urban mixed traffic. How an agent …