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Driven by Vision: Learning Navigation by Visual Localization and Trajectory Prediction.

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  • 1Institute of Mathematics of the Romanian Academy (IMAR), Calea Grivitei 21, 010702 Bucharest, Romania.

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Summary
This summary is machine-generated.

This study introduces a novel AI system for self-driving cars that navigates using only video, without GPS. It accurately predicts location and trajectory for reliable route planning in urban environments.

Keywords:
autonomous drivingautonomous driving datasetdeep learninggeometric computer visionlocalization by image segmentationself-drivingtrajectory predictionvisual localizationvisual navigation

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Human drivers utilize mental maps for navigation, often without external aids.
  • Current autonomous driving systems benefit from Global Positioning System (GPS) data.
  • A gap exists in autonomous navigation capabilities without real-time GPS.

Purpose of the Study:

  • To develop a self-driving system capable of route planning without GPS at inference time.
  • To enable real-time prediction of vehicle location and trajectory using only visual input.
  • To advance autonomous navigation research through visual data processing.

Main Methods:

  • A novel approach for visual localization and navigation from raw video streams.
  • Real-time prediction of steering commands and long-term navigation decisions.
  • Introduction of a large urban dataset with video and processed GPS streams for supervision.

Main Results:

  • The proposed system outperforms state-of-the-art methods in visual localization and steering.
  • Demonstrated reliable navigation assistance between arbitrary known locations.
  • Showcased adaptability to varying weather conditions and urban environmental changes.

Conclusions:

  • Autonomous navigation is feasible using only visual input and a known map, without GPS.
  • The developed system offers robust performance and adaptability in complex urban settings.
  • Public release of the dataset and code will foster further research in visual navigation.