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The acquisition of machine learning (ML) datasets by meas-urements for automotive radar data requires many resources and time. On the other hand, the simulation of complex traffic environments with sufficient level-of-detail is challenging, too. In this letter, a middle way is proposed in which real measurements are virtually augmented by reflections obtained from simulation models. The augmented measurements are then replayed with a hardware-based radar target simulator (RTS). This enables the fast creation of application-specific datasets, as well as advanced functionality tests of algorithms at deployment. To demonstrate the efficacy of the virtually augmented radar data, a set of test drives is augmented by virtual pedestrians performing traffic gestures. Then, a classifier trained on the created dataset is demonstrated to achieve a high classification accuracy of 84.0&#x0025; on real test data.

Virtually Augmented Radar Measurements With Hardware Radar Target Simulators for Machine Learning Applications