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arXiv AI By Noura Fady, Farah Khaled, Catherine M. Elias

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

Read the original on arXiv AI →

The paper introduces an automated pipeline that converts non‑critical driving scenes into safety‑critical scenarios by integrating computer vision, Large Language Models (LLMs), and Augmented Reality (AR). It detects and tracks road users, extracts safety features such as distance, velocity, motion direction, and Time‑to‑Collision (TTC), and evaluates scene criticality. Safe scenes are then modified by an LLM, which generates realistic collision‑inducing objects and behaviors that are overlaid onto the original scene using AR, achieving 97.52% safety classification accuracy on the nuScenes dataset and producing realistic scenarios like pedestrian crossings, rear overtaking vehicles, and sudden‑stop events.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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