Optical Smart-Wheel Sensing of Tire Deformation for Tire State and Load Estimation

Abstract

Tire–road interaction is increasingly important for situational awareness in autonomous vehicles and advanced driver-assistance systems, as tire deformation contains information that is related to both vehicle kinematics and tire forces. Optical sensing offers a direct means of observing this interaction with minimal influence on tire dynamics. In this study, an optical smart-wheel prototype was developed using a single high-speed camera rotating with the wheel and observing a circular-marker grid applied to the tire’s inner liner. Camera calibration and distance-dependent imaging tests were used to support geometric correction and pixel-to-length conversion, after which the system was evaluated using static and on-road dynamic tests. Static drum calibration established a relationship between the diagonal-distance metric and vertical wheel load (R2 = 0.9992; RMSE = 33.37 N). Initial vehicle-level multi-point validation over 30 static conditions and loads up to approximately 3.92 kN yielded a prediction R2 of 0.9933 and an RMSE of 91.05 N. Dynamic tests revealed increasing longitudinal and lateral deformation with increasing maneuver speed and decreasing inflation pressure, together with direction-dependent lateral responses consistent with wheel-load transfer. Across the cases included in the cycle-variation analysis, the coefficients of variation ranged from 1.88% to 5.10% for longitudinal amplitude and from 6.08% to 19.98% for lateral peak deformation. These results demonstrate sensitivity to operating conditions and indicate a potential association between lateral deformation and slip angle, rather than constituting direct tire-force or slip-angle estimation. The proposed metric-based approach provides a promising lower-complexity alternative to full-field stereo-based deformation measurement for future real-time tire-state sensing and investigation of force-related deformation indicators.