用对比学习提取驾驶风格，生成更像真人、更安全的自动驾驶测试行为。
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving
- 通过对比学习从数据中提取离散驾驶风格
- 生成行为比基线方法更安全且更贴近人类
- 适合用于提升自动驾驶系统测试的真实性
从数据中学习准确且丰富的拟人驾驶行为以用于自动驾驶测试仍具挑战，因人类驾驶风格高度多样且变化大。本文提出一种新方法，利用对比学习从已有驾驶数据中提取驾驶风格字典，并通过量化实现风格离散化，进而训练条件扩散策略以模拟人类驾驶员。实证评估表明，该方法生成的行为在安全性与人类相似性上均优于基于机器学习的基线方法。我们相信此方法能显著提升自动驾驶系统的测试真实性和评估有效性。
原文摘要 · Abstract (English)
Learning to perform accurate and rich simulations of human driving behaviors from data for autonomous vehicle testing remains challenging due to human driving styles' high diversity and variance. We address this challenge by proposing a novel approach that leverages contrastive learning to extract a dictionary of driving styles from pre-existing human driving data. We discretize these styles with quantization, and the styles are used to learn a conditional diffusion policy for simulating human drivers. Our empirical evaluation confirms that the behaviors generated by our approach are both safer and more human-like than those of the machine-learning-based baseline methods. We believe this has the potential to enable higher realism and more effective techniques for evaluating and improving the performance of autonomous vehicles.
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