Data-driven Diffusion Models for Enhancing Safety in Autonomous Vehicle Traffic Simulations

TL;DR

This paper introduces a novel adversarially guided diffusion model that improves the realism and effectiveness of safety-critical traffic scenario generation for autonomous vehicle testing.

Contribution

It presents a new approach integrating adversarial guidance into diffusion models to better capture driver behavior and traffic density complexities.

Findings

Outperforms existing methods in effectiveness

Produces more realistic traffic scenarios

Enhances safety validation for autonomous vehicles

Abstract

Safety-critical traffic scenarios are integral to the development and validation of autonomous driving systems. These scenarios provide crucial insights into vehicle responses under high-risk conditions rarely encountered in real-world settings. Recent advancements in critical scenario generation have demonstrated the superiority of diffusion-based approaches over traditional generative models in terms of effectiveness and realism. However, current diffusion-based methods fail to adequately address the complexity of driver behavior and traffic density information, both of which significantly influence driver decision-making processes. In this work, we present a novel approach to overcome these limitations by introducing adversarial guidance functions for diffusion models that incorporate behavior complexity and traffic density, thereby enhancing the generation of more effective and realistic safety-critical traffic scenarios. The proposed method is evaluated on two evaluation metrics: effectiveness and realism.The proposed method is evaluated on two evaluation metrics: effectiveness and realism, demonstrating better efficacy as compared to other state-of-the-art methods.