Generating Out-Of-Distribution Scenarios Using Language Models

TL;DR

This paper presents a novel framework leveraging Large Language Models to generate diverse and realistic out-of-distribution driving scenarios for autonomous vehicle testing, enhancing safety validation and model robustness.

Contribution

It introduces a LLM-based scenario generation method that creates a branching tree of OOD scenarios, evaluated with new diversity and OOD-ness metrics, and assesses VLMs' ability to interpret these scenarios.

Findings

Generated diverse OOD scenarios with high richness.

New metrics effectively quantify scenario diversity and deviation.

VLMs can interpret and navigate simulated OOD scenarios.

Abstract

The deployment of autonomous vehicles controlled by machine learning techniques requires extensive testing in diverse real-world environments, robust handling of edge cases and out-of-distribution scenarios, and comprehensive safety validation to ensure that these systems can navigate safely and effectively under unpredictable conditions. Addressing Out-Of-Distribution (OOD) driving scenarios is essential for enhancing safety, as OOD scenarios help validate the reliability of the models within the vehicle's autonomy stack. However, generating OOD scenarios is challenging due to their long-tailed distribution and rarity in urban driving dataset. Recently, Large Language Models (LLMs) have shown promise in autonomous driving, particularly for their zero-shot generalization and common-sense reasoning capabilities. In this paper, we leverage these LLM strengths to introduce a framework for generating diverse OOD driving scenarios. Our approach uses LLMs to construct a branching tree, where each branch represents a unique OOD scenario. These scenarios are then simulated in the CARLA simulator using an automated framework that aligns scene augmentation with the corresponding textual descriptions. We evaluate our framework through extensive simulations, and assess its performance via a diversity metric that measures the richness of the scenarios. Additionally, we introduce a new "OOD-ness" metric, which quantifies how much the generated scenarios deviate from typical urban driving conditions. Furthermore, we explore the capacity of modern Vision-Language Models (VLMs) to interpret and safely navigate through the simulated OOD scenarios. Our findings offer valuable insights into the reliability of language models in addressing OOD scenarios within the context of urban driving.