At AVS Lab we are thrilled to announce the release of our latest innovation in autonomous driving technology: the Extro-Spective Prediction (ESP) Dataset. This new dataset is a milestone in our ongoing efforts to enhance safety and reliability in fully autonomous vehicles, particularly in emergency scenarios.
The Challenge of Emergency Scenario Prediction
Ensuring safety in emergent-scene scenarios is crucial for the development of autonomous driving systems. Reliable, on-time prediction of these scenarios is essential, yet it remains a formidable challenge. Emergency events are long-tailed and difficult to collect data for, which has historically hindered the development of robust prediction models.
To tackle this issue, we have developed the ESP-Dataset, which focuses on long-term prediction by capturing inconspicuous state variations in historical data for emergency events. This innovative approach enables us to improve the prediction accuracy of such critical scenarios, thereby enhancing overall safety.
Key Features of the ESP-Dataset
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Extensive Data Collection: The ESP-Dataset encompasses data collected over 2,000 kilometers, specifically targeting challenging scenarios involving emergency events. This extensive coverage ensures a comprehensive dataset that can effectively support the development of advanced prediction models.
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Semantic Environment Information: Our dataset includes detailed semantic environment information, providing a rich context for each recorded scenario. This enhances the quality and depth of the data, making it more valuable for predictive modeling.
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New Evaluation Metric - CTE: We have introduced a new metric, named Comprehensive Time-sensitive Evaluation (CTE), designed to provide a thorough assessment of prediction performance in emergency scenarios. This metric offers a robust framework for evaluating the effectiveness of prediction algorithms under time-sensitive conditions.
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Enhanced Feature Extraction and Network Encoder: The ESP feature extraction and network encoder are designed to integrate seamlessly with existing backbones and algorithms. These enhancements enable researchers and developers to bolster the capabilities of current systems, driving further improvements in autonomous driving technology.
If you are interested in learning more about the ESP Dataset, you can checkout the most imporant information here: