Research on Data Fusion of Positioning System with a Fault Detection Mechanism for Autonomous Vehicles
Source abstract
Abstract
A positioning system provides useful positioning information for applications. The ability to maintain high-quality positioning information in real time is crucial. Autonomous vehicles require positioning information to plan control strategies. Inadequate positioning accuracy affects control and safety. This article explores the effect of an error detection and correction method on coordinate information fusion. This method can be used to efficiently correct positioning errors in the presence of faults. The fault detection mechanism is not limited to positioning systems with certain types of sensor applications. and it has certain flexibility in various applications. This positioning system detection method uses more than two positioning systems. Primarily, one positioning system is used to monitor the other. When the error of the positioning system differs excessively from the system setting value, the positioning fusion algorithm will make appropriate adjustments to output the corrected coordinate value. Therefore, this method integrates different positioning technologies, including a global position system (GPS), simultaneous localization and mapping (SLAM), inertial measurement units (IMUs), and mileage meters. Autonomous vehicles already have the required sensors deployed, and our method can be implemented at no additional cost. We integrated two positioning systems to support the positioning applications and designed a fault detection method to detect errors in the positioning system. This allowed the system to have some reference information by which to correct the coordinate information. According to the experimental findings, positioning information errors were corrected at a rate of 89.8%. The positioning system with the added detection function was able to effectively filter the data without too many errors. Additionally, the results showed that this method can effectively correct positioning errors. The results indicate that adding an error detection mechanism can effectively improve the accuracy of coordinate data fusion. Positioning systems with an error detection function can thus improve applications’ performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotics and Sensor-Based Localization · Autonomous Vehicle Technology and Safety · Automated Road and Building Extraction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Ph.D. Program of Electrical and Communication Engineering, Feng-Chia University, Taichung 40724, Taiwan
Autmotive Research and Testing Center, Changhua 50544, Taiwan
Department of Electronic Engineering, Feng-Chia University, Taichung 40724, Taiwan
Funding
Intelligent Vehicle Technology and Autonomous Driving System Development Project
References (26)
Show source-provided references
1. Cao, D., Zhao, B., Han, H., and Gan, W. (2008, January 20–21). Design on Remote Diagnosis System of Refrigerated Containers. Proceedings of the 2008 International Symposium on Knowledge Acquisition and Modeling, Wuhan, China.10.1109/KAM.2008.105 ↗
2. (2022, June 06). Available online: https://www.artc.org.tw/tw/research/detail/39.
3. Reliable and redundant RTK positioning for applications in hard observational conditions
4. Speth, T., Kamann, A., Brandmeier, T., and Jumar, U. (2016, January 19–20). Precise relative ego-positioning by stand-alone RTK-GPS. Proceedings of the 13th Workshop Positioning Navigation Communications (WPNC), Bremen, Germany.10.1109/WPNC.2016.7822852 ↗
5. RTK GPS
6. Autonomous vehicle based in cooperative GPS and inertial systems10.1017/S0263574708004232 ↗
7. Interacting multiple model filter-based sensor fusion of GPS with in-vehicle sensors for real-time vehicle positioning10.1109/TITS.2011.2171033 ↗
8. Song, Y., Li, Q., Li, B., Xu, J., and Zheng, N. (2006, January 17–20). Real-Time Dynamic Display on Spatial Data for Car Navigation Service. Proceedings of the International Conference on Intelligent Transportation System Telecommunications Proceedings, Toronto, ON, Canada.10.1109/ITST.2006.288788 ↗
9. VPS-SLAM: Visual planar semantic SLAM for aerial robotic systems10.1109/ACCESS.2020.2983121 ↗
10. Tateno, K., Tombari, F., Laina, I., and Navab, N. (2017). CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction. IEEE CVPR, 6243–6252.10.1109/CVPR.2017.695 ↗
11. (2022, September 11). Available online: https://www.artc.org.tw/tw/research/detail/32.
12. Wang, L., Zhang, Y., and Wang, J. (2017, January 14). Map-based localization method for autonomous vehicles using 3D-LIDAR. Proceedings of the 20th IFAC World Congress, Toulouse, France.10.1016/j.ifacol.2017.08.046 ↗
13. Kohlbrecher, S., Stryk, O., Meyer, J., and Klingauf, U. (2011, January 1–5). A Flexible and Scalable SLAM System with Full 3D Motion Estimation. Proceedings of the International Conference on Safety Security and Rescue Robotics (SSRR), Kyoto, Japan.10.1109/SSRR.2011.6106777 ↗
14. A 3D LiDAR data-based dedicated road boundary detection algorithm for autonomous vehicles10.1109/ACCESS.2019.2902170 ↗
15. Brock, O., Trinkle, F., and Ramos, J. (2009). Model-Based Vehicle Tracking for Autonomous Driving in Urban Environments, MIT Press.
16. (2015). Road Vehicles—Controller Area Network (CAN)—Part 1: Data Link Layer and Physical Signaling (Standard No. ISO 11898-1:2016).
17. (2016). Road Vehicles—Controller Area Network (CAN)—Part 2: High-Speed Medium Access Unit (Standard No. ISO 11898-1:2016).
18. (2016). Road vehicles—Controller Area Network (CAN)—Part 3: Low-Speed, Fault-Tolerant, Medium-Dependent Interface (Standard No. ISO 11898-1:2006).
19. Miletiev, R., Iontchev, E., and Yordanov, R. (2022, January 11–15). Multisensor precision positioning based on inertial and GNSS systems via 5G network. Proceedings of the 45th International Spring Seminar on Electronics Technology (ISSE), Vienna, Austria.10.1109/ISSE54558.2022.9812792 ↗
20. Mostafavi, S.S., Sorrentino, S., Guldogan, M.B., and Fodor, G. (2020, January 7–11). Vehicular Positioning Using 5G Millimeter Wave and Sensor Fusion in Highway Scenarios. Proceedings of the IEEE International Conference on Communications (ICC), Dublin, Ireland.10.1109/ICC40277.2020.9149067 ↗
21. Eckermann, F., Gorczak, P., and Wietfeld, C. (2020, January 25–28). Cooperative Validation of CAM Position Information Using C-V2X. Proceedings of the IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), Antwerp, Belgium.10.1109/VTC2020-Fall49728.2020.9348703 ↗
22. Hedges, C., and Perry, F. (2008). Overview and Use of SAE j2735 Message Sets for Commercial Vehicles, SAE Technical Paper. No. 2008-01-2650.10.4271/2008-01-2650 ↗
23. El-Shafie, A., Hussain, A., and Eldin, A.E.N. (2009, January 13–15). ANFIS-Based Model for Real-time INS/GPS Data Fusion for Vehicular Navigation System. Proceedings of the International Conference on Computer Technology and Development, Kota Kinabalu, Malaysia.10.1109/ICCTD.2009.42 ↗
24. Raveena, C., Sravya, R., Kumar, R., and Chavan, A. (2020, January 6–9). Sensor Fusion Module Using IMU and GPS Sensors For Autonomous Car. Proceedings of the IEEE INOCON, Virtual Conference.10.1109/INOCON50539.2020.9298316 ↗
25. Min, H., Wu, X., Cheng, C., and Zhao, X. (2019). Kinematic and dynamic vehicle model-assisted global positioning method for autonomous vehicles with low-cost GPS/camera/in-vehicle sensors. Sensors, 19.10.3390/s19245430 ↗
26. Landmark and IMU data fusion: Systematic convergence geometric nonlinear observer for SLAM and velocity bias10.1109/TITS.2020.3035550 ↗