Diffusion Models for Safety Validation of Autonomous Driving Systems

TL;DR

This paper introduces a diffusion model that generates realistic failure scenarios for autonomous vehicles, aiding safety validation without needing external datasets or extensive resources.

Contribution

The authors develop a diffusion-based approach for autonomous driving safety validation that can generate diverse failure cases without prior system knowledge or external data.

Findings

Generates realistic failure samples at traffic intersections

Captures a wide variety of potential failures

Operates with modest computational resources

Abstract

Safety validation of autonomous driving systems is extremely challenging due to the high risks and costs of real-world testing as well as the rarity and diversity of potential failures. To address these challenges, we train a denoising diffusion model to generate potential failure cases of an autonomous vehicle given any initial traffic state. Experiments on a four-way intersection problem show that in a variety of scenarios, the diffusion model can generate realistic failure samples while capturing a wide variety of potential failures. Our model does not require any external training dataset, can perform training and inference with modest computing resources, and does not assume any prior knowledge of the system under test, with applicability to safety validation for traffic intersections.