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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.12230 |
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| _version_ | 1866916005403426816 |
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| author | Schäfke, Hendrik Weber, Daniel O. M. Vagapov, Askar Schweers, Christoph Seel, Thomas Ehlers, Simon F. G. |
| author_facet | Schäfke, Hendrik Weber, Daniel O. M. Vagapov, Askar Schweers, Christoph Seel, Thomas Ehlers, Simon F. G. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12230 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neural Network-Based Virtual Wheel-Speed Sensor for Enhanced Low-Velocity State Estimation Schäfke, Hendrik Weber, Daniel O. M. Vagapov, Askar Schweers, Christoph Seel, Thomas Ehlers, Simon F. G. Systems and Control 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. |
| title | Neural Network-Based Virtual Wheel-Speed Sensor for Enhanced Low-Velocity State Estimation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2605.12230 |