Abnormal Driving Area Detection Using Multiple Vehicle Dynamic Model-Based Filter: Design and Experimental Validation
Abstract
:1. Introduction
2. Related Work
- The dynamic behavior of the vehicle, which changes according to the change of the driving environment, is expressed as a parameter change of the system model.
- It is possible to indirectly check the status of the vehicle even on the unvisited and unknown area from the probability value obtained through the interaction of multiple models.
- The proposed method is the first approach for 6x6 skid-type AGV and the overall procedure of the proposed method was experimentally validated via a multi-purpose AGV on the rough proving ground test.
3. Vehicle Model
3.1. Lateral Vehicle Dynamics
3.2. Cornering Stiffness
4. Interacting Multiple Model
4.1. Multiple Model Setting
4.2. Interaction of Multiple Model
4.3. Filtering
4.3.1. Prediction
4.3.2. Correction
4.4. Updating
4.5. Merging
4.6. Indexing
5. Experiment Results
5.1. Experiment Setup
5.2. Results Analysis
- Case 1. Wet ground after rain, and worn tires.
- Case 2. Dry ground, and new tires.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Kang, C.; Lee, T.; Shin, J. Abnormal Driving Area Detection Using Multiple Vehicle Dynamic Model-Based Filter: Design and Experimental Validation. Machines 2024, 12, 564. https://doi.org/10.3390/machines12080564
Kang C, Lee T, Shin J. Abnormal Driving Area Detection Using Multiple Vehicle Dynamic Model-Based Filter: Design and Experimental Validation. Machines. 2024; 12(8):564. https://doi.org/10.3390/machines12080564
Chicago/Turabian StyleKang, Changmook, Taehyung Lee, and Jongho Shin. 2024. "Abnormal Driving Area Detection Using Multiple Vehicle Dynamic Model-Based Filter: Design and Experimental Validation" Machines 12, no. 8: 564. https://doi.org/10.3390/machines12080564