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As automated vehicles are getting closer to becoming a reality, it willbecome mandatory to be able to characterise the performance of their obstacledetection systems. This validation process requires large amounts ofground-truth data, which is currently generated by manually annotation. In thispaper, we propose a novel methodology to generate ground-truth kinematicsdatasets for specific objects in real-world scenes. Our procedure requires noannotation whatsoever, human intervention being limited to sensors calibration.We present the recording platform which was exploited to acquire the referencedata and a detailed and thorough analytical study of the propagation of errorsin our procedure. This allows us to provide detailed precision metrics for eachand every data item in our datasets. Finally some visualisations of theacquired data are given.

Automatic generation of ground truth for the evaluation of obstacle detection and tracking techniques