Miniature Autonomy as One Important Testing Means in the Development of Machine Learning Methods for Autonomous Driving : How ML-based Autonomous Driving could be Realized on a 1:87 Scale - Tim Tiedemann and Jonas Fuhrmann and Sebastian Paulsen and Thorben Schnirpel and Nils Schönherr and Bettina Buth and Stephan Pareigis (SCITEPRESS - Science and Technology Publications, 2019)
In the current state of autonomous driving machine learning methods are dominating, especially for the environment recognition. For such solutions, the reliability and the robustness is a critical question. A “miniature autonomy” with model vehicles at a small scale could be beneficial for different reasons. Examples are (1) the testability of dangerous and close-to-crash edge cases, (2) the possibility to test potentially dangerous concepts as end-to-end learning or combined inference and learning phases, (3) the need to optimize algorithms thoroughly, and (4) a potential reduction of test mile counts. Presented is the motivation for miniature autonomy and a discussion of testing of machine learning methods. Finally, two currently set up platforms including one with an FPGA-based TPU for ML acceleration are described.
Keywords: Autonomy; Autonomous Driving; System Level Tests; Machine Learning; Mobile Robot