Showing 1–1 of 1 results for author: Schweers, C

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

Authors: Hendrik Schäfke, Daniel O. M. Weber, Askar Vagapov, Christoph Schweers, Thomas Seel, Simon F. G. Ehlers

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 bot… ▽ More 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. △ Less

Submitted 12 May, 2026; originally announced May 2026.

Comments: Accepted for publication in the Proceedings of the 22nd IFAC World Congress, Busan, Republic of Korea, 2026