複雑な知覚タスクのシミュレーション評価を効率化する手法
A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation
計算コストの高い深層学習モデルを軽量な代理モデルに置き換え、低忠実度シミュレータで大規模テストを効率化する手法を提案し、自動運転のLiDAR検出器で有効性を示した。
著者: Jonathan Sadeghi, Blaine Rogers, James Gunn, Thomas Saunders, Sina Samangooei, Puneet Kumar Dokania, John Redford
分類: cs.LG, cs.AI, cs.CV, cs.RO
原文アブストラクト
There has been increasing interest in characterising the error behaviour of systems which contain deep learning models before deploying them into any safety-critical scenario. However, characterising such behaviour usually requires large-scale testing of the model that can be extremely computationally expensive for complex real-world tasks. For example, tasks involving compute intensive object detectors as one of their components. In this work, we propose an approach that enables efficient large-scale testing using simplified low-fidelity simulators and without the computational cost of executing expensive deep learning models. Our approach relies on designing an efficient surrogate model corresponding to the compute intensive components of the task under test. We demonstrate the efficacy of our methodology by evaluating the performance of an autonomous driving task in the Carla simulator with reduced computational expense by training efficient surrogate models for PIXOR and CenterPoint LiDAR detectors, whilst demonstrating that the accuracy of the simulation is maintained.
