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We instead frame the trajectory prediction problem as classification over a diverse set of trajectories.
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.
The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform.
#2 best model for Multiple Object Tracking on KITTI Tracking test
To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.).
Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle deployed in complex traffic.
In practice, our approach reduces the average prediction error by more than 54% over prior algorithms and achieves a weighted average accuracy of 91. 2% for behavior prediction.
SOTA for Trajectory Prediction on Argoverse
Better machine understanding of pedestrian behaviors enables faster progress in modeling interactions between agents such as autonomous vehicles and humans.
#2 best model for Trajectory Prediction on ETH/UCY
We show through experiments on real and synthetic data that the proposed method leads to generate more diverse samples and to preserve the modes of the predictive distribution.
Developing safe human-robot interaction systems is a necessary step towards the widespread integration of autonomous agents in society.