Integrity Monitoring of Multimodal Perception System for Vehicle Localization
Abstract
:1. Introduction
2. Problem Statement
Contributions
- Defining a common reference frame and formalizing a common model to represent all data sources in all scenarios.
- Prototyping an integrity assessment framework using the common model and providing proof of concept.
- Analyzing the performance of the proposed framework using publicly available datasets and comparison with other state-of-the-art integrity monitoring solutions from the literature.
3. Methodology
3.1. Detection
3.1.1. Vision
3.1.2. LiDAR
3.2. Map Handling
3.3. Representation
3.4. Integrity Analysis
3.5. Localization Optimization
Algorithm 1 Algorithm for localization optimization. |
Inputs: Localization: , GPS+IMU localization measurement: , FG of LiDAR: L, FG of Vision: C, FG of Map: M, Minimum coherence limit: |
if and are consistent then |
if and then |
Output: Integrity markers |
Update |
else |
Compute: |
if and are consistent then |
Output Integrity values |
Update |
else |
if then |
Output: Integrity markers |
else |
Output: Error in map |
end if |
end if |
end if |
else |
Output: Error in GPS |
end if |
4. Experiments and Discussions
- : Not enough nodes in the map for model fitting;
- : Not enough lane markings for model fitting;
- : GPS measurement is not available or an outlier;
- : Vehicle not moving or moving very slowly;
- : Vehicle performing a hard turn.
4.1. Integrity Marker Comparison
4.2. Complex Situations
4.3. Performance of Integrity Monitoring
5. Conclusions
6. Future Works
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Dataset–Frames | Integrity [18] | Integrity (Ours) | Situation |
---|---|---|---|
Dataset 1–150 | map-0.422 | not enough nodes from the map | |
Dataset 1–21 | vision-0.175 | vision-0.612 | no good quality lane markings |
Dataset 2–390 | map-0.087 | map-0.374 | road with multiple curvatures |
Dataset 3–562 | vision-0.573 | partial occulusion in vision due to vehicles | |
Dataset 3–1117 | map-0.006 | map-0.381 | wrong map extraction |
Dataset 4-22 | vision-0.214 | vision-0.681 | road with multiple curvatures |
Dataset 4-260 | vision-0.651 | vision-0.629 | highway road with single curvature |
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Balakrishnan, A.; Florez, S.R.; Reynaud, R. Integrity Monitoring of Multimodal Perception System for Vehicle Localization. Sensors 2020, 20, 4654. https://doi.org/10.3390/s20164654
Balakrishnan A, Florez SR, Reynaud R. Integrity Monitoring of Multimodal Perception System for Vehicle Localization. Sensors. 2020; 20(16):4654. https://doi.org/10.3390/s20164654
Chicago/Turabian StyleBalakrishnan, Arjun, Sergio Rodriguez Florez, and Roger Reynaud. 2020. "Integrity Monitoring of Multimodal Perception System for Vehicle Localization" Sensors 20, no. 16: 4654. https://doi.org/10.3390/s20164654
APA StyleBalakrishnan, A., Florez, S. R., & Reynaud, R. (2020). Integrity Monitoring of Multimodal Perception System for Vehicle Localization. Sensors, 20(16), 4654. https://doi.org/10.3390/s20164654