Safe Control Allocation of Articulated Heavy Vehicles Using Machine Learning
- Delivery
- Available on the other site
- Click here to order.
- Paper/Info type
- AVEC
- Pages
- 1-7(Total 7 p)
- Date of publication
- Sep 2024
- Publisher
- Others, Unknown
- Language
- English
- Event
- AVEC '24
Detailed Information
| Author(E) | 1) Sander van Dam, 2) Lukas Wisell, 3) Kartik Shingade, 4) Mikael Kieu, 5) Umur Erdinc, 6) Maliheh Sadeghi Kati, 7) Esteban Gelso, 8) Dhasarathy Parthasarathy |
|---|---|
| Affiliation(E) | 1) Volvo Group Trucks Technology / Chalmers University of Technology, 2) Volvo Group Trucks Technology / Chalmers University of Technology, 3) Volvo Group Trucks Technology / Chalmers University of Technology, 4) Volvo Group Trucks Technology, 5) Volvo Group Trucks Technology / Chalmers University of Technology, 6) Volvo Group Trucks Technology, 7) Volvo Group Trucks Technology, 8) Volvo Group Trucks Technology |
| Abstract(E) | As articulated heavy vehicles are over-actuated, achieving a safe control allocation is crucial to ensure stability. This study introduces a machine learning model developed to identify unsafe behaviours and modes, such as jack-knifing and trailer swing, enabling the control scheme to prioritize stability. High-fidelity simulations, focusing on highrisk scenarios, generate data for training the machine learning model. This model is integrated into the control scheme to predict safe braking allocations and prevent unsafe vehicle modes during real-time driving scenarios. Initial tests showed promising results regarding prediction accuracy and a safety margin that can be implemented to further ensure that safe vehicle motion is achieved. |
