motion prediction

The UCY dataset consist of real pedestrian trajectories with rich multi-human interaction scenarios captured at 2.5 Hz (Δt=0.4s). It is composed of three sequences (Zara01, Zara02, and UCY), taken in public spaces from top-view. Source: Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data Image Source: http://trajnet.stanford.edu/data.php?n=1

As autonomous driving systems mature, motion forecasting has received increasing attention as a critical requirement for planning. Of particular importance are interactive situations such as merges, unprotected turns, etc., where predicting individual object motion is not sufficient. Joint predictions of multiple objects are required for effective route planning. There has been a critical need for high-quality motion data that is rich in both interactions and annotation to develop motion planning models. In this work, we introduce the most diverse interactive motion dataset to our knowledge, and provide specific labels for interacting objects suitable for developing joint prediction models. With over 100,000 scenes, each 20 seconds long at 10 Hz, our new dataset contains more than 570 hours of unique data over 1750 km of roadways. It was collected by mining for interesting interactions between vehicles, pedestrians, and cyclists across six cities within the United States. We use a high-accuracy 3D auto-labeling system to generate high quality 3D bounding boxes for each road agent, and provide corresponding high definition 3D maps for each scene. Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models. Finally, we provide strong baseline models for individual-agent prediction and joint-prediction. We hope that this new large-scale interactive motion dataset will provide new opportunities for advancing motion forecasting models.

The Argoverse 2 Motion Forecasting Dataset is a curated collection of 250,000 scenarios for training and validation. Each scenario is 11 seconds long and contains the 2D, birds-eye-view centroid and heading of each tracked object sampled at 10 Hz. To curate this collection, we sifted through thousands of hours of driving data from our fleet of self-driving test vehicles to find the most challenging segments. We place special emphasis on kinematically and socially unusual behavior, especially when exhibited by actors relevant to the ego-vehicle’s decision-making process. Some examples of interactions captured within our dataset include: buses navigating through multi-lane intersections, vehicles yielding to pedestrians at crosswalks, and cyclists sharing dense city streets. Spanning 2,000+ km over six geographically diverse cities, Argoverse 2 covers a large geographic area. Argoverse 2 also contains a large object taxonomy with 10 non-overlapping classes that encompass a broad range of actors, both static and dynamic. In comparison to the Argoverse 1 Motion Forecasting Dataset, the scenarios in this dataset are approximately twice as long and more diverse. Together, these changes incentivize methods that perform well on extended forecast horizons, handle multiple types of dynamic objects, and ensure safety in long tail scenarios.

Extreme Pose Interaction (ExPI) Dataset is a new person interaction dataset of Lindy Hop dancing actions. In Lindy Hop, the two dancers are called leader and follower. The authors recorded two couples of dancers in a multi-camera setup equipped also with a motion-capture system. 16 different actions are performed in ExPI dataset, some by the two couples of dancers, some by only one of the couples. Each action was repeated five times to account for variability. More precisely, for each recorded sequence, ExPI provides: (i) Multi-view videos at 25FPS from all the cameras in the recording setup; (ii) Mocap data (3D position of 18 joints for each person) at 25FPS synchronized with the videos.; (iii) camera calibration information; and (iv) 3D shapes as textured meshes for each frame. Overall, the dataset contains 115 sequences with 30k visual frames for each viewpoint and 60k 3D instances annotated

The SIND dataset is based on 4K video captured by drones, providing information including traffic participant trajectories, traffic light status, and high-definition maps

The GTA Indoor Motion dataset (GTA-IM) that emphasizes human-scene interactions in the indoor environments. It consists of HD RGB-D image sequences of 3D human motion from a realistic game engine. The dataset has clean 3D human pose and camera pose annotations, and large diversity in human appearances, indoor environments, camera views, and human activities. Source: https://github.com/ZheC/GTA-IM-Dataset Image Source: https://github.com/ZheC/GTA-IM-Dataset

A self-driving dataset for motion prediction, containing over 1,000 hours of data. This was collected by a fleet of 20 autonomous vehicles along a fixed route in Palo Alto, California, over a four-month period. It consists of 170,000 scenes, where each scene is 25 seconds long and captures the perception output of the self-driving system, which encodes the precise positions and motions of nearby vehicles, cyclists, and pedestrians over time. Source: One Thousand and One Hours: Self-driving Motion Prediction Dataset

NTU RGB+D 2D is a curated version of NTU RGB+D often used for skeleton-based action prediction and synthesis. It contains less number of actions.

Covers 5 generic driving scenarios, with a total of 25 distinct action classes. It contains more than 15K full HD, 5s long videos acquired in various driving conditions, weathers, daytimes and environments, complemented with a common and realistic set of sensor measurements. This amounts to more than 2.25M frames, each annotated with an action label, corresponding to 600 samples per action class. Source: VIENA2: A Driving Anticipation Dataset

Consists of two pedestrian trajectory datasets, CITR dataset and DUT dataset, so that the pedestrian motion models can be further calibrated and verified, especially when vehicle influence on pedestrians plays an important role. CITR dataset consists of experimentally designed fundamental VCI scenarios (front, back, and lateral VCIs) and provides unique ID for each pedestrian, which is suitable for exploring a specific aspect of VCI. DUT dataset gives two ordinary and natural VCI scenarios in crowded university campus, which can be used for more general purpose VCI exploration. Source: Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus

I2-2000FPS is the first high-speed video dataset offering an unprecedented temporal resolution of 2000 frames per second (fps). Captured using the commercially available Chronos 1.4 high-speed CMOS camera, the dataset includes a diverse range of objects varying in size, shape, orientation, and motion, as well as various camera movements. This dataset is designed to enable research in areas such as motion analysis, object tracking, and scene understanding at extreme temporal resolutions. Potential applications span fields like sports analysis, robotics, autonomous navigation, and high-speed videography.

Occ-Traj120 is a trajectory dataset that contains occupancy representations of different local-maps with associated trajectories. This dataset contains 400 locally-structured maps with occupancy representation and roughly around 120K trajectories in total. Source: https://github.com/soraxas/Occ-Traj120 Image Source: https://github.com/soraxas/Occ-Traj120

This dataset is a result of a study that was created to assess drivers behaviors when following a lead vehicle. The driving simulator study used a simulated suburban environment for collecting driver behavior data while following a lead vehicle driving through various unsignalized intersections. The driving environment had two lanes in each direction and a dedicated left-turn lane for the intersection. The experiment was deployed on a miniSim Driving Simulator. We programmed the lead vehicle ran- domly turn left, right or go straight through the intersections. In total we had 2(traffic density) × 2(speed level) × 3 = 12 scenarios for each participant to be tested on. We split the data into train, validation and test sets. The setup for the task is to observe 1 second of trajectories and predict the next 3,5 and 8 seconds.

The Freiburg Street Crossing dataset consists of data collected from three different street crossings in Freiburg, Germany; ; two of which were traffic light regulated intersections and one a zebra crossing without traffic lights. The data can be used to train agents to cross roads autonomously. Source: http://aisdatasets.informatik.uni-freiburg.de/streetcrossing/ Image Source: http://aisdatasets.informatik.uni-freiburg.de/streetcrossing/