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Variational autoencoders simulate vehicle drivetrain signals effectively

By PulseAugur Editorial · [1 sources] ·

Researchers have developed variational autoencoders (VAEs) to simulate vehicle jerk signals from torque demand, addressing limitations in real-world drivetrain data. The VAEs, trained on data from electric SUVs, can generate realistic jerk signals that capture various drivetrain scenarios without needing detailed system parameters. This approach offers an alternative to costly experiments and manual modeling, potentially speeding up vehicle development by aiding data augmentation and scenario exploration. AI

IMPACT Potential to streamline vehicle validation and accelerate development through improved simulation.

RANK_REASON This is a research paper detailing a novel application of VAEs for drivetrain simulation.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Variational autoencoders simulate vehicle drivetrain signals effectively

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This is a research paper detailing a novel application of VAEs for drivetrain simulation.
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162 days old
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pallavi Sharma, Jorge-Humberto Urrea-Quintero, Bogdan Bogdan, Adrian-Dumitru Ciotec, Laura Vasilie, Henning Wessels, Matteo Skull ·

    Drivetrain simulation using variational autoencoders

    arXiv:2501.17653v3 Announce Type: replace Abstract: This work proposes variational autoencoders (VAEs) to predict a vehicle's jerk signals from torque demand in the context of limited real-world drivetrain datasets. We implement both unconditional and conditional VAEs, trained on…

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