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- W2897076737 abstract "Autonomous vehicles rely on many sensors in order for the vehicles to perceive theirsurroundings. Consequently it is important with safety verification of the sensors whichtypically is done by collecting data in many different scenarios which is time consumingand expensive. For this reason, autonomous driving software companies are interested invirtual verification where the scenarios are simulated. In this thesis we have developedand used the Recurrent Conditional Generative Adversarial Network (RCGAN) in orderto model the longitudinal error of sensors. The RCGAN is a modification of the originalgenerative adversarial network (GAN) framework which makes use of recurrent neuralnetworks and conditioning the networks on auxiliary information. These changes allowsthe model to learn and be able to generate realistic real-valued multi-dimensional timeseries." @default.
- W2897076737 created "2018-10-26" @default.
- W2897076737 creator A5087522707 @default.
- W2897076737 date "2018-01-01" @default.
- W2897076737 modified "2023-09-26" @default.
- W2897076737 title "Sensor Modelling with Recurrent Conditional GANs" @default.
- W2897076737 hasPublicationYear "2018" @default.
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