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Vision-Based Multi-Future Trajectory Prediction: A Survey.

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Summary

This summary is machine-generated.

This study surveys multi-future trajectory prediction (MTP), a key task for autonomous systems. It addresses diverse and uncertain human behaviors by generating multiple plausible future paths for agents.

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Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

Autonomous systems require accurate trajectory prediction for safe navigation.
Human behavior is inherently uncertain, leading to multiple possible future paths.
Existing methods often struggle to capture the diversity of future trajectories.

Purpose of the Study:

To provide the first comprehensive survey of multi-future trajectory prediction (MTP).
To introduce novel taxonomies for classifying MTP frameworks.
To analyze existing MTP datasets, evaluation metrics, and models.

Main Methods:

Systematic review and categorization of MTP approaches.
Comparative analysis of state-of-the-art models on benchmark datasets.
Experimental evaluation on the ForkingPath dataset.

Main Results:

Established unique taxonomies for MTP frameworks.
Compared and analyzed performance across various MTP models and datasets.
Identified key challenges and limitations in current MTP research.

Conclusions:

MTP is crucial for enabling diverse, acceptable, and explainable predictions in autonomous systems.
The survey provides a foundation for future research in MTP and related tasks.
Future directions include developing novel MTP systems and addressing diverse learning tasks.