LATIDIA · Robótica

LLM-Guided Transformation of Non-Critical Driving Scenes into Safety-Critical Scenarios Using Augmented Reality

arXiv: 2609.20318v1Announce Type: new Abstract: Testing Autonomous Driving Systems (Ads) requiere escenarios críticos de seguridad realistas, pero la recopilación de dichos datos de la conducción en el mundo real es costosa e insegura. Este documento presenta

La noticia

arXiv:2609.20318v1 Announce Type: new Abstract: Testing Autonomous Driving Systems (ADS) requires realistic safety-critical scenarios, but collecting such data from real-world driving is costly and unsafe. This paper presents an automated pipeline that transforms safe driving scenes into safety-critical scenarios by combining computer vision, Large Language Models (LLMs), and Augmented Reality (AR). The system detects and tracks road users, extracts safety features including distance, velocity, motion direction, and Time-to-Collision (TTC), and assesses scene criticality. Safe scenes are modified by an LLM, which generates realistic collision-inducing objects and behaviors that are integrated into the original scene using AR. The proposed pipeline was evaluated on the nuScenes dataset, achieving 97.52% safety classification accuracy