12 datasets found

The dataset used in the paper is the NFL Football dataset, which contains player and ball trajectories from the 2017 NFL season.

The dataset used in the paper is the NBA SportVU dataset, which contains player and ball trajectories from the 2015-2016 NBA season.

The dataset used in the paper is a physical simulation dataset, where the ground truth of the interaction relationship can be accessed.

The nuScenes dataset is a large-scale dataset for autonomous driving, containing 100,000 scenes with 10 Hz sampling frequency.

The Apolloscape dataset is a large-scale dataset for autonomous driving, containing 75 scenarios with 10 Hz sampling frequency.

The Argoverse 2 motion forecasting dataset contains 250,000 driving scenarios, each 11 seconds long. These scenarios cover 6 geographical regions and represent 763 total hours...

The Trajnet++ benchmark provides a fixed data split and unified pre-processing for these datasets.

Trajectory prediction is a crucial task for the safe operation of an autonomous vehicle. The task is known as human trajectory prediction and aims to predict the future...

Trajectory prediction plays an essential role in autonomous vehicles. While numerous strategies have been developed to enhance the robustness of trajectory prediction models,...

nuScenes: A multimodal dataset for autonomous driving.

The ETH and UCY datasets consist of various motions of 1,536 pedestrians across five unique scenes: ETH, Hotel, Univ, Zara1 and Zara2. They are recorded in crowded scenes.

The Google Research Football environment is a reinforcement learning experimental platform focused on training agents to play football.