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Semantic scene upgrades for trajectory prediction.

Arsal Syed1, Brendan Tran Morris1

  • 1Department of Electrical Engineering, University of Nevada Las Vegas, 4505 S Maryland Parkway, Box 454026, Las Vegas, NV 89154-4026 USA.

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Summary
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Understanding pedestrian motion is crucial for autonomous systems. Explicit scene semantics from segmented maps significantly improve trajectory prediction accuracy compared to other scene representations.

Keywords:
Deep learningSemantic segmentationTrajectory predictionVariational auto encoder

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Human-Motion Analysis

Background:

  • Pedestrian trajectory prediction is vital for autonomous driving and robot navigation.
  • Current research heavily focuses on spatial and social interactions, neglecting scene context.
  • Autonomous agents need comprehensive environmental understanding, including scene dependencies.

Purpose of the Study:

  • To investigate the impact of scene understanding on pedestrian trajectory prediction.
  • To evaluate different encoding mechanisms for incorporating scene information.
  • To determine the most effective scene representation for improving prediction accuracy.

Main Methods:

  • Utilized a recurrent Variational Autoencoder (VAE) model.
  • Encoded pedestrian motion history, social interactions, and semantic scene data.
  • Compared trajectory prediction performance using various scene representations: fully segmented maps, semantic maps, and CNN embeddings.

Main Results:

  • Fully segmented maps, providing explicit scene semantics, yielded superior performance.
  • This approach outperformed other scene representation methods in trajectory prediction.
  • The model successfully integrated motion, social, and scene data for enhanced predictions.

Conclusions:

  • Explicit scene semantics derived from fully segmented maps are highly effective for trajectory prediction.
  • Incorporating detailed scene information significantly enhances the understanding of pedestrian motion.
  • This research highlights the importance of scene context in developing robust autonomous navigation systems.