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This study introduces the Sim-to-Real Evaluation Benchmark (S2R-Bench) for autonomous driving perception systems. It addresses the gap in current benchmarks by using real-world data to improve the robustness of self-driving safety.

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Area of Science:

  • Autonomous Driving Systems
  • Computer Vision
  • Robotics

Background:

  • Perception algorithms are critical for autonomous driving safety but lack robustness.
  • Existing benchmarks fail to replicate real-world conditions like extreme weather and sensor anomalies.
  • Evaluating perception algorithm reliability is an emerging challenge.

Purpose of the Study:

  • To propose a novel Sim-to-Real Evaluation Benchmark (S2R-Bench) for autonomous driving.
  • To address the limitations of purely simulated benchmarks.
  • To foster research into more robust perception models for autonomous vehicles.

Main Methods:

  • Collected diverse sensor anomaly data across various real-world road and weather conditions.
  • Developed a benchmark dataset encompassing varied lighting intensities and time periods.
  • Compared real-world data with simulated data to validate reliability.

Main Results:

  • The S2R-Bench is the first corruption robustness dataset based on real-world scenarios.
  • Demonstrated the reliability of the collected real-world data for evaluation.
  • Highlighted the discrepancy between simulated and real-world performance.

Conclusions:

  • The S2R-Bench provides a realistic evaluation for autonomous driving perception systems.
  • The dataset facilitates the development of more robust perception algorithms.
  • This work is crucial for advancing the safety and reliability of autonomous driving technology.