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Online Quantitative Analysis of Perception Uncertainty Based on High-Definition Map.

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Summary
This summary is machine-generated.

This study introduces a novel method for evaluating perception uncertainty in autonomous driving using high-definition maps. The approach effectively assesses both static and dynamic element uncertainties for enhanced driving safety.

Keywords:
autonomous drivinghigh-definition mapperception uncertaintyuncertainty assessment

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Autonomous driving relies heavily on environmental perception for safe decision-making.
  • Evaluating perception uncertainty in real-time is a critical challenge for autonomous system safety and industrial adoption.
  • High-definition maps provide valuable prior information on static road elements and their relationships, aiding uncertainty assessment.

Purpose of the Study:

  • To develop and present a method for online evaluation of perception uncertainty for both static and dynamic elements in autonomous driving.
  • To leverage high-definition maps to improve the accuracy and reliability of perception uncertainty assessment.
  • To identify real-time factors influencing perception uncertainty to optimize assessment models.

Main Methods:

  • Assessed static element perception uncertainty using spatial and topological features from high-definition maps, considering existence and spatial information.
  • Developed an online model to assess dynamic element perception uncertainty, inferring it from static element uncertainty.
  • Constructed a model to recognize driving scenarios and weather conditions to identify real-time uncertainty triggers.

Main Results:

  • The proposed method effectively evaluates real-time perception results by assessing uncertainties of both static and dynamic elements.
  • Verification on the nuScenes dataset demonstrated the efficacy of the high-definition map-based uncertainty assessment.
  • The system successfully identified driving scenarios and weather conditions impacting perception uncertainty.

Conclusions:

  • The developed method provides a robust approach for online perception uncertainty evaluation in autonomous driving.
  • Utilizing high-definition maps significantly enhances the assessment of static element uncertainty, which in turn improves dynamic element uncertainty evaluation.
  • Real-time identification of influencing factors allows for adaptive optimization of perception uncertainty models, contributing to safer autonomous operations.