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Conditional Generative Models for Dynamic Trajectory Generation and Urban Driving.

David Paz1, Hengyuan Zhang1, Hao Xiang1

  • 1Autonomous Vehicle Laboratory, Contextual Robotics Institute, University of California San Diego, La Jolla, CA 92093, USA.

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Summary
This summary is machine-generated.

This study presents a new method for autonomous driving trajectory generation using OpenStreetMaps (OSM) data. It enables navigation in dynamic urban environments with fewer map details, offering real-time performance.

Keywords:
HD mapsautonomous drivingcoarse mapsgenerative modelsglobal planningperceptionscene understandingsemantic maps

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Area of Science:

  • Robotics
  • Computer Science
  • Artificial Intelligence

Background:

  • Current autonomous driving systems often rely on high-definition (HD) maps.
  • Dynamic urban environments present challenges due to frequent changes.
  • Minimizing reliance on detailed map priors is crucial for robust navigation.

Purpose of the Study:

  • To develop dynamic trajectory generation methodologies for urban driving.
  • To reduce the dependency on detailed map information for autonomous navigation.
  • To create a conditional generative model for urban driving behaviors.

Main Methods:

  • Utilized coarse global plan representations for trajectory generation.
  • Compared various OpenStreetMaps (OSM) data representations.
  • Formulated a conditional generative model to capture multimodal driving characteristics.
  • Collected data using full-scale vehicles with ground truth labels.

Main Results:

  • Demonstrated potential use cases for dynamic urban driving.
  • Achieved real-time performance in complex scenarios.
  • Validated the effectiveness of the proposed generative model strategy.

Conclusions:

  • The proposed approach offers a viable alternative to HD map-dependent systems.
  • The methodology is suitable for real-time autonomous navigation in dynamic urban settings.
  • The released dataset and code facilitate further research and development.