Neural Network-Based Virtual Wheel-Speed Sensor for Enhanced Low-Velocity State Estimation

TL;DR

This paper introduces a neural network-based virtual wheel-speed sensor that fuses wheel and motor signals, significantly reducing errors and providing smooth, real-time speed estimates for vehicle control.

Contribution

It presents a novel neural network model validated on real vehicle data, outperforming traditional sensors and filters in low-velocity state estimation.

Findings

Achieves up to 85% error reduction compared to production sensors.

Reduces errors by 47% over optimized zero-phase filters.

Demonstrates robust generalization across diverse driving maneuvers.

Abstract

Accurate wheel speed information is crucial for vehicle control and state estimation. Conventional sensors suffer from quantization and latency, especially at low velocities, while motor-speed signals in electric vehicles are distorted by drivetrain torsion. This work presents a neural-network-based virtual wheel-speed sensor that fuses wheel-speed and motor-speed signals to reduce errors from both sources. Validated on real-world Volkswagen ID.7 data, the real-time capable model achieves an error reduction of up to 85% compared to the production sensor and 47% compared to an optimized zero-phase filter, providing a smooth signal for driver-assistance functions. The results demonstrate robust generalization across diverse real-world maneuvers within the vehicle platform.