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.

翻译：从数据中学习准确且丰富的人类驾驶行为模拟，以用于自动驾驶车辆测试，仍然面临挑战，这主要源于人类驾驶风格的高度多样性和差异性。为应对这一挑战，我们提出一种新方法，该方法利用对比学习从已有的人类驾驶数据中提取驾驶风格字典。我们通过量化对这些风格进行离散化处理，并利用这些风格来学习一个用于模拟人类驾驶员的条件扩散策略。我们的实证评估证实，与基于机器学习的基线方法相比，本方法生成的行为既更安全，也更接近人类驾驶。我们相信，这有望为实现更高真实度和更有效的自动驾驶车辆性能评估与提升技术提供可能。