mlsanityData to 13 Oct 2026

VeMo: A Lightweight Data-Driven Approach to Model Vehicle Dynamics

arXiv 2510.07447 ↗First posted 8 Oct 2025cs.RO cs.LG math.DScited by 0

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Abstract (arXiv)

Developing a dynamic model for a high-performance vehicle is a complex problem that requires extensive structural information about the system under analysis. This information is often unavailable to those who did not design the vehicle and represents a typical issue in autonomous driving applications, which are frequently developed on top of existing vehicles; therefore, vehicle models are developed under conditions of information scarcity. This paper proposes a lightweight encoder-decoder model based on Gate Recurrent Unit layers to correlate the vehicle's future state with its past states, measured onboard, and control actions the driver performs. The results demonstrate that the model achieves a maximum mean relative error below 2.6% in extreme dynamic conditions. It also shows good robustness when subject to noisy input data across the interested frequency components. Furthermore, being entirely data-driven and free from physical constraints, the model exhibits physical consistency in the output signals, such as longitudinal and lateral accelerations, yaw rate, and the vehicle's longitudinal velocity.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 9 of its 38 references that are in the map. First posted on arXiv 8 Oct 2025.

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References · 9

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,024
  2. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,061
  3. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  4. 2018Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentationcited by 16
  5. 2024Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy datacited by 15
  6. 2022Physics Embedded Neural Network Vehicle Model and Applications in Risk-Aware Autonomous Driving Using Latent Featurescited by 12
  7. 2023Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Constrained Neural Network for Autonomous Racingcited by 6
  8. 2021Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Predictioncited by 4
  9. 2021Data-driven Nonlinear Model Reduction to Spectral Submanifolds in Mechanical Systemscited by 3

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