Fig 1.
Tire Contact Interface.
Fig 2.
Key Structure Diagram.
Table 1.
Quantum Topology Feature Extraction Architecture.
Fig 3.
Quantum Topology.
Fig 4.
Meta Learning Adapter.
Table 2.
Specific Algorithm Steps.
Table 3.
Topology-physical mapping calibration results.
Table 4.
Manifold curvature-friction coefficient mapping calibration results.
Table 5.
Observability verification of topological variables.
Table 6.
Topology-physical mapping improves control performance.
Fig 5.
(a) Parameter Drift (b) Startup Delay (c) Failure rate.
Table 7.
Test Condition Parameter Matrix.
Table 8.
Technical Parameters of RNMST Intelligent Tire.
Table 9.
Real Vehicle Test Condition Matrix.
Fig 6.
(a) Slip Ratio (b) Response Delay (c) Energy consumption.
Table 10.
Environmental Simulation Cabin Test Parameters.
Table 11.
Dynamic impact test data.
Table 12.
Response to friction sudden changes in operating conditions.
Fig 7.
(a) Response time (b) Overshoot (c) Control Error.
Table 13.
Electromagnetic compatibility test results.
Table 14.
Comparison of Bridge Vibration Suppression Performance.
Table 15.
Tire Health Diagnostic Parameters.
Table 16.
Economic Analysis of Intelligent Road Network.
Table 17.
Performance Comparison of Quantum Topology Meta Learning with Traditional ABS and Model Predictive Control.