Safety-Aware Hardening of 3D Object Detection Neural Network Systems
This work addresses safety-critical applications like autonomous driving by incrementally enhancing existing detection systems with safety mechanisms.
The paper tackles the problem of making 3D object detection neural networks safety-aware by introducing a safety specification that partitions input space by criticality and incorporates robustness criteria, resulting in a specialized loss function and safety-aware post-processing algorithm.
We study how state-of-the-art neural networks for 3D object detection using a single-stage pipeline can be made safety aware. We start with the safety specification (reflecting the capability of other components) that partitions the 3D input space by criticality, where the critical area employs a separate criterion on robustness under perturbation, quality of bounding boxes, and the tolerance over false negatives demonstrated on the training set. In the architecture design, we consider symbolic error propagation to allow feature-level perturbation. Subsequently, we introduce a specialized loss function reflecting (1) the safety specification, (2) the use of single-stage detection architecture, and finally, (3) the characterization of robustness under perturbation. We also replace the commonly seen non-max-suppression post-processing algorithm by a safety-aware non-max-inclusion algorithm, in order to maintain the safety claim created by the neural network. The concept is detailed by extending the state-of-the-art PIXOR detector which creates object bounding boxes in bird's eye view with inputs from point clouds.