Trajectory Forecasting

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

The highD dataset is a new dataset of naturalistic vehicle trajectories recorded on German highways. Using a drone, typical limitations of established traffic data collection methods such as occlusions are overcome by the aerial perspective. Traffic was recorded at six different locations and includes more than 110 500 vehicles. Each vehicle's trajectory, including vehicle type, size and manoeuvres, is automatically extracted. Using state-of-the-art computer vision algorithms, the positioning error is typically less than ten centimeters. Although the dataset was created for the safety validation of highly automated vehicles, it is also suitable for many other tasks such as the analysis of traffic patterns or the parameterization of driver models.

The inD dataset is a new dataset of naturalistic vehicle trajectories recorded at German intersections. Using a drone, typical limitations of established traffic data collection methods like occlusions are overcome. Traffic was recorded at four different locations. The trajectory for each road user and its type is extracted. Using state-of-the-art computer vision algorithms, the positional error is typically less than 10 centimetres. The dataset is applicable on many tasks such as road user prediction, driver modeling, scenario-based safety validation of automated driving systems or data-driven development of HAD system components.

JAAD is a dataset for studying joint attention in the context of autonomous driving. The focus is on pedestrian and driver behaviors at the point of crossing and factors that influence them. To this end, JAAD dataset provides a richly annotated collection of 346 short video clips (5-10 sec long) extracted from over 240 hours of driving footage. These videos filmed in several locations in North America and Eastern Europe represent scenes typical for everyday urban driving in various weather conditions. Bounding boxes with occlusion tags are provided for all pedestrians making this dataset suitable for pedestrian detection. Behavior annotations specify behaviors for pedestrians that interact with or require attention of the driver. For each video there are several tags (weather, locations, etc.) and timestamped behavior labels from a fixed list (e.g. stopped, walking, looking, etc.). In addition, a list of demographic attributes is provided for each pedestrian (e.g. age, gender, direction of motion, etc.) as well as a list of visible traffic scene elements (e.g. stop sign, traffic signal, etc.) for each frame. Paper: Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior Source: JAAD Image Source: Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior

The rounD dataset introduces a fresh compilation of natural road user trajectory data from German roundabouts, gathered using drone technology to navigate past usual challenges such as occlusions inherent in traditional traffic data collection methods. It includes traffic data from three unique locations, capturing the movement and categorizing each road user by type. Advanced computer vision algorithms are applied to ensure high positional accuracy. This dataset is highly adaptable for a variety of applications, including predicting road user behavior, driver modeling, scenario-based safety evaluations for automated driving systems, and the data-driven creation of Highly Automated Driving (HAD) system components.

The TrajNet Challenge represents a large multi-scenario forecasting benchmark. The challenge consists on predicting 3161 human trajectories, observing for each trajectory 8 consecutive ground-truth values (3.2 seconds) i.e., t−7,t−6,…,t, in world plane coordinates (the so-called world plane Human-Human protocol) and forecasting the following 12 (4.8 seconds), i.e., t+1,…,t+12. The 8-12-value protocol is consistent with the most trajectory forecasting approaches, usually focused on the 5-dataset ETH-univ + ETH-hotel + UCY-zara01 + UCY-zara02 + UCY-univ. Trajnet extends substantially the 5-dataset scenario by diversifying the training data, thus stressing the flexibility and generalization one approach has to exhibit when it comes to unseen scenery/situations. In fact, TrajNet is a superset of diverse datasets that requires to train on four families of trajectories, namely 1) BIWI Hotel (orthogonal bird’s eye flight view, moving people), 2) Crowds UCY (3 datasets, tilted bird’s eye view, camera mounted on building or utility poles, moving people), 3) MOT PETS (multisensor, different human activities) and 4) Stanford Drone Dataset (8 scenes, high orthogonal bird’s eye flight view, different agents as people, cars etc. ), for a total of 11448 trajectories. Testing is requested on diverse partitions of BIWI Hotel, Crowds UCY, Stanford Drone Dataset, and is evaluated by a specific server (ground-truth testing data is unavailable for applicants). Source: Transformer Networks for Trajectory Forecasting Image Source: http://trajnet.stanford.edu/

Supports new task that predicts future locations of people observed in first-person videos. Source: Future Person Localization in First-Person Videos

The exiD dataset introduces a groundbreaking collection of naturalistic road user trajectories at highway entries and exits in Germany, meticulously captured with drones to navigate past the limitations of conventional traffic data collection methods, such as occlusions. This approach not only allows for the precise extraction of each road user’s trajectory and type but also ensures very high positional accuracy, thanks to sophisticated computer vision algorithms. Its innovative data collection technique minimizes errors and maximizes the quality and reliability of the dataset, making it a valuable resource for advanced research and development in the field of automated driving technologies.

PIE is a new dataset for studying pedestrian behavior in traffic. PIE contains over 6 hours of footage recorded in typical traffic scenes with on-board camera. It also provides accurate vehicle information from OBD sensor (vehicle speed, heading direction and GPS coordinates) synchronized with video footage. Rich spatial and behavioral annotations are available for pedestrians and vehicles that potentially interact with the ego-vehicle as well as for the relevant elements of infrastructure (traffic lights, signs and zebra crossings). There are over 300K labeled video frames with 1842 pedestrian samples making this the largest publicly available dataset for studying pedestrian behavior in traffic.

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This dataset contains aircraft trajectories in an untowered terminal airspace collected over 8 months surrounding the Pittsburgh-Butler Regional Airport [ICAO:KBTP], a single runway GA airport, 10 miles North of the city of Pittsburgh, Pennsylvania. The trajectory data is recorded using an on-site setup that includes an ADS-B receiver. The trajectory data provided spans days from 18 Sept 2020 till 23 Apr 2021 and includes a total of 111 days of data discounting downtime, repairs, and bad weather days with no traffic. Data is collected starting at 1:00 AM local time to 11:00 PM local time. The dataset uses an Automatic Dependent Surveillance-Broadcast (ADS-B) receiver placed within the airport premises to capture the trajectory data. The receiver uses both the 1090 MHz and 978 MHz frequencies to listen to these broadcasts. The ADS-B uses satellite navigation to produce accurate location and timestamp for the targets which is recorded on-site using our custom setup. Weather data during the data collection time period is also included for environmental context. The weather data is obtained post-hoc using the METeorological Aerodrome Reports (METAR) strings generated by the Automated Weather Observing System (AWOS) system at KBTP. The raw METAR string is then appended to the raw trajectory data by matching the closest UTC timestamps. We also provide processed data that filters, interpolates and transforms data from a global frame to an airport-centred inertial frame. The inertial frame is centred at one end of the runway with the x-axis along the runway. Trajectories are filtered with aircrafts under 6000 ft MSL and around a 5km radius around the airport origin. We also remove duplicates and interpolate data every second. The proceed files also contain wind-data; a crucial factor in decision-making; separated in components along and perpendicular to the runway direction.

The NBA SportVU dataset contains player and ball trajectories for 631 games from the 2015-2016 NBA season. The raw tracking data is in the JSON format, and each moment includes information about the identities of the players on the court, the identities of the teams, the period, the game clock, and the shot clock.

The Euro-PVI dataset contains trajectories of pedestrians and bicyclists, with dense interactions with the ego-vehicle. The dataset is collected in Brussels and Leuven, Belgium. The goal of this dataset is to address the challenge of future trajectory prediction in urban environments with dense pedestrian (bicyclist) - vehicle interactions.

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.

METEOR is a complex traffic dataset which captures traffic patterns in unstructured scenarios in India. METEOR consists of more than 1000 one-minute video clips, over 2 million annotated frames with ego-vehicle trajectories, and more than 13 million bounding boxes for surrounding vehicles or traffic agents. METEOR is a unique dataset in terms of capturing the heterogeneity of microscopic and macroscopic traffic characteristics.

The uniD dataset is an innovative collection of naturalistic road user trajectories, captured within the RWTH Aachen University campus using drone technology to address common challenges such as occlusions found in traditional traffic data collection methods. It meticulously documents the movement and classifies each road user by type. Employing cutting-edge computer vision algorithms, the dataset ensures high positional accuracy. Its utility spans various applications, from predicting road user behavior and modeling driver actions to conducting scenario-based safety checks for automated driving systems and facilitating the data-driven design of Highly Automated Driving (HAD) system components.