Tnt: Target-driven trajectory prediction
Predicting the future behavior of moving agents is essential for real world applications. It is
challenging as the intent of the agent and the corresponding behavior is unknown and …
challenging as the intent of the agent and the corresponding behavior is unknown and …
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 …
Bat: Behavior-aware human-like trajectory prediction for autonomous driving
The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to
overcome on the journey to fully autonomous vehicles. To address this challenge, we …
overcome on the journey to fully autonomous vehicles. To address this challenge, we …
It is not the journey but the destination: Endpoint conditioned trajectory prediction
Human trajectory forecasting with multiple socially interacting agents is of critical importance
for autonomous navigation in human environments, eg, for self-driving cars and social …
for autonomous navigation in human environments, eg, for self-driving cars and social …
Adaptive trajectory prediction via transferable gnn
Pedestrian trajectory prediction is an essential component in a wide range of AI applications
such as autonomous driving and robotics. Existing methods usually assume the training and …
such as autonomous driving and robotics. Existing methods usually assume the training and …
Loki: Long term and key intentions for trajectory prediction
Recent advances in trajectory prediction have shown that explicit reasoning about agents'
intent is important to accurately forecast their motion. However, the current research …
intent is important to accurately forecast their motion. However, the current research …
Social-wagdat: Interaction-aware trajectory prediction via wasserstein graph double-attention network
Effective understanding of the environment and accurate trajectory prediction of surrounding
dynamic obstacles are indispensable for intelligent mobile systems (like autonomous …
dynamic obstacles are indispensable for intelligent mobile systems (like autonomous …
Multi-agent tensor fusion for contextual trajectory prediction
Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory
prediction is challenging because it requires reasoning about agents' past movements …
prediction is challenging because it requires reasoning about agents' past movements …
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 …
[HTML][HTML] Gatraj: A graph-and attention-based multi-agent trajectory prediction model
Trajectory prediction has been a long-standing problem in intelligent systems like
autonomous driving and robot navigation. Models trained on large-scale benchmarks have …
autonomous driving and robot navigation. Models trained on large-scale benchmarks have …