CornerBench: Is Your Driving Model Safe Enough?

Modern end-to-end driving planners have improved rapidly on common driving benchmarks, but whether they can safely handle rare, high-consequence corner cases remains unclear. Evaluating such cases is difficult because natural driving logs contain few safety-critical events, while staged data collection is costly and difficult to scale. We present **CornerFactory**, an automated pipeline for constructing corner cases from real-world driving logs according to structured scene specifications. CornerFactory supports multi-view-consistent object insertion, novel-view synthesis from perturbed ego poses, and transfer across vehicle platforms with different camera configurations. Building on this pipeline, we introduce **CornerBench**, a benchmark that organizes generated corner cases into a structured taxonomy and evaluates planners using continuous, severity-aware metrics that capture graded safety differences. Using CornerBench, we evaluate representative planners along several dimensions commonly expected to improve robustness, including training data volume and model size, multimodal sensing, language-based scene understanding and chain-of-thought reasoning in VLA planners, and the use of world models for future prediction and planning.