Improving the Safety of 3D Object Detectors in Autonomous Driving using IoGT and Distance Measures
State-of-the-art object detectors are commonly evaluated based on accuracy metrics such as mean Average Precision (mAP). In this paper, inspired by the fact that mAP is not a direct safety indicator, we propose a straightforward safety metric, especially for 3D object detectors in Autonomous Driving contexts, by combining the Intersection-over-Ground-Truth (IoGT) measure and a distance ratio. Subsequently, we formulate a safety-aware loss function by amending IoGT to commonly used accuracy-oriented loss functions. Our experiments using models from the MMDetection3D library, the nuScenes dataset, and an in-house simulation dataset demonstrate that the object detector trained with our loss function significantly reduces unsafe predictions while staying performant on accuracy and maintaining good stability in the learning process.
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