用扩散模型生成更真实安全的自动驾驶交通场景
Data-driven Diffusion Models for Enhancing Safety in Autonomous Vehicle Traffic Simulations
- 引入对抗性引导函数，融合驾驶行为复杂度与车流密度
- 在有效性和真实性上优于现有最先进方法
- 适合自动驾驶系统测试与安全性验证的研究者
安全关键型交通场景对自动驾驶系统的研发与验证至关重要，可提供高风险条件下车辆响应的重要信息，而这些情况在现实世界中极为罕见。近年来，基于扩散的方法在生成关键场景方面已展现出比传统生成模型更优的效果与真实性。然而，当前扩散方法未能充分考虑驾驶行为复杂性与交通密度信息，而这两者显著影响驾驶员决策过程。本文提出一种新方法，通过为扩散模型引入融合行为复杂度与交通密度的对抗性引导函数，以提升安全关键交通场景的生成效果。该方法在有效性与真实性两个评估指标上进行了验证，表现优于其他最先进方法。
原文摘要 · Abstract (English)
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.
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