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Fault Detection, Isolation, Identification and Recovery (FDIIR) Methods for Automotive Perception Sensors Including a

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Automated driving relies on dependable perception sensors. This review classifies sensor faults and recovery methods, finding single-sensor algorithms effective for fault detection and mitigation.

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Area of Science:

  • Automotive Engineering
  • Sensor Technology
  • Artificial Intelligence

Background:

  • Automated driving systems depend on perception sensors like cameras, radar, and lidar.
  • Sensor reliability is critical for safety, especially under adverse conditions.
  • Implementing fault diagnosis and mitigation is essential for advancing automated driving.

Purpose of the Study:

  • To systematically review faults in automotive perception sensors.
  • To identify and classify detection and recovery methods for these faults.
  • To highlight research gaps and opportunities in sensor fault management.

Main Methods:

  • Systematic literature analysis focused on lidar sensors.
  • Review of existing fault detection and recovery algorithms.
  • Development of a classification schema for sensor faults.

Main Results:

  • Adverse weather conditions are the most studied fault category, but recovery methods are often lacking.
  • Sensor attachment and mechanical damage are under-researched fault areas.
  • Single-sensor data stream algorithms show promise for both fault detection and recovery.

Conclusions:

  • A comprehensive classification schema for automotive perception sensor faults is proposed.
  • Further research is needed on sensor attachment and mechanical damage faults.
  • Single-sensor algorithms offer a viable path for robust fault management in perception systems.