A Machine Learning Perspective on Automated Driving Corner Cases

• URL: http://arxiv.org/abs/2510.10653v1
• Date: Sun, 12 Oct 2025 15:18:12 GMT
• Title: A Machine Learning Perspective on Automated Driving Corner Cases
• Authors: Sebastian Schmidt, Julius Körner, Stephan Günnemann,
• Abstract summary: We propose a novel machine learning approach that takes the underlying data distribution into account.<n>We present a framework for effective corner case recognition for perception on individual samples.
• Score: 47.42055037776276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For high-stakes applications, like autonomous driving, a safe operation is necessary to prevent harm, accidents, and failures. Traditionally, difficult scenarios have been categorized into corner cases and addressed individually. However, this example-based categorization is not scalable and lacks a data coverage perspective, neglecting the generalization to training data of machine learning models. In our work, we propose a novel machine learning approach that takes the underlying data distribution into account. Based on our novel perspective, we present a framework for effective corner case recognition for perception on individual samples. In our evaluation, we show that our approach (i) unifies existing scenario-based corner case taxonomies under a distributional perspective, (ii) achieves strong performance on corner case detection tasks across standard benchmarks for which we extend established out-of-distribution detection benchmarks, and (iii) enables analysis of combined corner cases via a newly introduced fog-augmented Lost & Found dataset. These results provide a principled basis for corner case recognition, underlining our manual specification-free definition.

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