Improving Robustness of Semantic Segmentation for Autonomous Driving: A Case Study
Abstract
Deep neural networks (DNNs) have achieved remarkable success in recent years and are increasingly integrated into safety-critical systems such as autonomous driving vehicles. However, when deployed in real-world environments, their robustness to common input corruptions remains a major challenge for safety assurance. Corruptions such as motion blur can change the outputs of DNN-based semantic segmentation models and, more importantly, cause unsafe system-level decision inconsistencies, for example by failing to identify ground obstacles that are correctly recognized under clean conditions. In this paper, we present a testing-oriented robustness repair approach for semantic segmentation models in real-world industrial settings. We first use corruption-based testing to reveal decision-level failures under realistic perturbations, and then repair the model through a combination of data augmentation and self-training using only unlabeled data. Rather than focusing solely on pixel-level prediction changes, our method targets the reduction of system violations while preserving decision behavior on clean inputs. We evaluate the approach on a semantic segmentation model used in an industrial product. Experimental results show that our method significantly reduces system violation rates while maintaining system-level decision-making accuracy, demonstrating the practical value of testing-guided repair for safety-critical deployment.