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A New Approach to Unwanted-Object Detection in GNSS/LiDAR-Based Navigation
Mathieu Joerger1, Guillermo Duenas Arana2, Matthew Spenko3
1College of Aerospace & Mechanical Engineering, The University of Arizona, Tucson, AZ 85721, USA. joerger@email.arizona.edu.
Sensors (Basel, Switzerland)
|August 22, 2018
Summary
This study introduces advanced safety risk assessment for integrated Global Navigation Satellite System (GNSS) and LiDAR navigation in highly automated vehicles (HAVs). New methods improve detection of unwanted objects, enhancing navigation integrity and continuity.
Area of Science:
- Automotive Engineering
- Robotics
- Navigation Systems
Background:
- Highly automated vehicles (HAVs) rely on integrated Global Navigation Satellite System (GNSS) and LiDAR for precise navigation.
- Existing safety risk assessment for GNSS/LiDAR navigation has limitations, particularly in feature extraction (FE) and data association (DA).
- Prior methods assumed perfect matching between extracted features and mapped landmarks, neglecting potential discrepancies.
Purpose of the Study:
- To develop novel methods for assessing safety risks in integrated GNSS/LiDAR navigation systems for HAVs.
- To address limitations of previous risk prediction algorithms by detecting unwanted objects (UO).
- To derive an integrity risk bound accounting for the undetected UO.
Main Methods:
- Incorporation of a Kalman filter innovation-based test to detect unwanted objects (UO).
- UO detection includes unmapped, moving, and wrongly excluded landmarks.
- Derivation of an integrity risk bound to quantify the risk associated with undetected UO.
Main Results:
- Quantification of the impact of UO monitoring on navigation integrity and continuity.
- Validation through direct simulations and preliminary testing of the developed methods.
- Demonstration of improved safety risk assessment for GNSS/LiDAR systems.
Conclusions:
- The developed methods enhance the safety and reliability of integrated GNSS/LiDAR navigation for HAVs.
- Effective UO monitoring is crucial for maintaining navigation integrity and continuity.
- The integrity risk bound provides a critical measure for ensuring safe operation of automated vehicles.
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