Multi-sensor Fusion Road Friction Coefficient Estimation During Steering with Lyapunov Method.

Area of Science:

• Automotive Engineering
• Control Systems
• Road Surface Characterization

Background:

• Accurate road friction coefficient estimation is crucial for autonomous vehicle safety and dynamic control.
• Advancements in vehicle sensors provide opportunities for improved environmental perception and parameter estimation.

Purpose of the Study:

• To develop and validate a nonlinear observer for estimating the road friction coefficient using vehicle lateral displacement information.
• To enhance tire models for high friction conditions and integrate them into the estimation algorithm.

Main Methods:

• Modified the tire brush model using tire test data for high friction conditions.
• Designed a nonlinear observer based on vehicle dynamics and kinematic models.
• Utilized self-aligning torque, lateral acceleration, and lateral displacement for estimation during steering.

Main Results:

• The proposed nonlinear observer effectively estimates the road friction coefficient.
• The algorithm demonstrated rapid convergence to the reference value in high friction conditions.
• Slalom and Double Line Change (DLC) tests validated the algorithm's performance during steering.

Conclusions:

• The developed nonlinear observer provides a robust method for real-time road friction coefficient estimation.
• The approach enhances the capabilities of autonomous vehicles in varying road conditions.
• The method shows significant potential for improving vehicle dynamic control and safety systems.