EMT Dataset
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EMT Benchmark Datasets
| Dataset | Description |
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Tracking Benchmark The Tracking Benchmark is meticulously designed to assess the performance of algorithms in consistently identifying and maintaining object tracking over time within the dynamic complexities of driving environments. This benchmark evaluates the motion of various road users, including vehicles, pedestrians, cyclists, and motorbikes, captured from a forward-facing camera. It challenges models to perform reliably under diverse traffic conditions, such as high congestion levels, unpredictable pedestrian movements, and frequent lane changes. The dataset encompasses 8,806 unique tracking IDs, featuring: 8,076 vehicles, 568 pedestrians, 158 motorbikes and 14 cyclists. With an average tracking duration of 6.5 seconds, this benchmark sets a rigorous standard for evaluating tracking algorithms in real-world traffic scenarios. |
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Trajectory Prediction Benchmark The Trajectory Prediction Benchmark focuses on testing the capability of forecasting models to predict the movement of agents in heterogeneous and dynamic traffic conditions. This includes interactions between vehicles and other road users at large intersections, roundabouts, and other multi-agent environments. It aims to challenge models in effectively generalizing across diverse scenarios and accurately predicting future trajectories. This dataset features 1,016 unique agents, consisting of 906 vehicles and 86 pedestrians. The benchmark incorporates past trajectories and prediction horizons, employing a shifting window approach to simulate real-time forecasting challenges. It serves as a vital resource for developing models that enhance the safety and planning capabilities of autonomous systems. |
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Intention Prediction Benchmark The Intention Prediction Benchmark is crafted to test the ability of autonomous systems to predict the future actions of surrounding traffic participants. By analyzing current trajectories, positions, and the surrounding context, this benchmark enables systems to anticipate the movements of nearby agents with precision. The dataset offers comprehensive annotations for vehicle maneuvers and pedestrian behaviors. For vehicles, the labeled maneuvers capture high-level intended actions that shape future trajectories, including:Turn left/right, Keep lane, Merge (left/right), Brake, Stop and Reverse. For pedestrians, the dataset provides four behavior categories: Waiting to cross, Crossing, Walking (on sidewalks or pavements), Stopping (e.g., waiting at bus stops). In total, the dataset comprises 1,520 sequences of agent trajectories with detailed intention labels, offering a rich resource for understanding and predicting complex behaviors in diverse traffic scenarios. |