用于自动驾驶中扩散策略的离散对比学习
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving
AI总结：
本文提出利用对比学习从人类驾驶数据中提取并量化驾驶风格字典，进而学习条件扩散策略来模拟人类驾驶行为，实验表明其生成的行为比基线方法更安全且更像人类。
摘要：
由于人类驾驶风格具有高度多样性和差异性，从数据中学习对人类驾驶行为进行准确且丰富的模拟以用于自动驾驶车辆测试仍然具有挑战性。我们通过提出一种新颖的方法来应对这一挑战，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。我们通过量化将这些风格离散化，并使用这些风格来学习用于模拟人类驾驶者的条件扩散策略。我们的实证评估证实，与基于机器学习的基线方法相比，我们的方法生成的行为既更安全，也更像人类。我们相信，这有潜力实现更高的真实感，并为评估和改进自动驾驶车辆的性能提供更有效的技术。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
