Investigation on the Model-Based Control Performance in Vehicle Safety Critical Scenarios with Varying Tyre Limits

Area of Science:

• Vehicle dynamics and control systems engineering.
• Automotive engineering and virtual prototyping.
• Advanced driver-assistance systems (ADAS) and autonomous driving.

Background:

• Current virtual driver prototypes have limitations, often prioritizing worst-case scenarios over optimal performance.
• Calibration is restricted to predefined conditions, reducing effectiveness in critical safety situations.
• Environmental factors (weather, road surface) and tire conditions (thermal, wear) significantly impact vehicle adherence and dynamics.

Purpose of the Study:

• To investigate physical model-based control for virtual driver systems.
• To incorporate dynamic system behavior and boundary condition variations into control strategies.
• To enhance the reliability and performance of virtual driver models by considering multi-physical tire variations.

Main Methods:

• Development and implementation of a physical model-based control algorithm.
• Testing various scenarios with specific tire thermal and wear conditions on diverse road surfaces.
• Validation using a hardware-in-the-loop (HIL) real-time environment.

Main Results:

• Demonstrated augmented reliability of a virtual driver aware of tire dynamic limits.
• Validated the model predictive control algorithm in a real-time HIL environment.
• Showcased the ability to account for multi-physical tire variations and boundary conditions.

Conclusions:

• Physical model-based control offers a paradigm shift for developing advanced driving automation strategies.
• This approach enhances virtual driver models by exploiting physical modeling for improved vehicle control.
• The study provides a breakthrough towards more reliable and capable automated driving systems by considering dynamic tire variations.