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State-of-the-art object detectors are commonly evaluated based on accuracymetrics such as mean Average Precision (mAP). In this paper, inspired by thefact that mAP is not a direct safety indicator, we propose a straightforwardsafety metric, especially for 3D object detectors in Autonomous Drivingcontexts, by combining the Intersection-over-Ground-Truth (IoGT) measure and adistance ratio. Subsequently, we formulate a safety-aware loss function byamending IoGT to commonly used accuracy-oriented loss functions. Ourexperiments using models from the MMDetection3D library, the nuScenes dataset,and an in-house simulation dataset demonstrate that the object detector trainedwith our loss function significantly reduces unsafe predictions while stayingperformant on accuracy and maintaining good stability in the learning process.

Improving the Safety of 3D Object Detectors in Autonomous Driving using IoGT and Distance Measures