Simulation of the real world is a widely researchedtopic in various fields. The automotive industry in particular isvery dependent on real world simulations, since these simulations are needed in order to prove the safety of advance driverassistance systems (ADAS) and autonomous driving (AD). Inthis paper we propose a deep learning based model for simulating the outputs from production sensors used in autonomousvehicles. We introduce an improved Recurrent ConditionalGenerative Adversarial Network (RC-GAN) consisting of Recurrent Neural Networks (RNNs) that use Long Short-TermMemory (LSTM) in both the generator and the discriminatornetworks in order to generate production sensor errors thatexhibit long-term temporal correlations. The network is trainedin a sequence-to-sequence fashion where we condition theoutput from the model on sequences describing the surroundingenvironment. This enables the model to capture spatial andtemporal dependencies, and the model is used to generatesynthetic time series describing the errors in a productionsensor which can be used for more realistic simulations. Themodel is trained on a data set collected from real roads withvarious traffic settings, and yields significantly better results ascompared to previous works.