Deep generative models such as conditional variational autoencoders (CVAEs) have shown great promise for predicting trajectories of surrounding agents in autonomous vehicle planning. State-of-the-art models have achieved remarkable accuracy in such prediction tasks. Besides accuracy, diversity is also crucial for safe planning because human behaviors are inherently uncertain and multimodal. However, existing methods generally lack a scheme to generate controllably diverse trajectories, which is arguably more useful than randomly diversified trajectories, to the end of safe planning. To address this, we propose PrefCVAE, an augmented CVAE framework that uses weakly labeled preference pairs to imbue latent variables with semantic attributes. Using average velocity as an example attribute, we demonstrate that PrefCVAE enables controllable, semantically meaningful predictions without degrading baseline accuracy. Our results show the effectiveness of preference supervision as a cost-effective way to enhance sampling-based generative models.

翻译：条件变分自编码器（CVAEs）等深度生成模型在自动驾驶规划中预测周围智能体轨迹方面展现出巨大潜力。在此类预测任务中，最先进的模型已实现卓越的精度。除精度外，多样性对于安全规划同样至关重要，因为人类行为本质上是具有不确定性和多模态的。然而，现有方法普遍缺乏生成可控多样化轨迹的机制——相较于随机多样化的轨迹，可控多样化轨迹对安全规划而言无疑更具实用价值。为此，我们提出PrefCVAE，一种增强型CVAE框架，它利用弱标注的偏好配对将语义属性注入隐变量。以平均速度为例属性，我们证明PrefCVAE能够在不降低基线精度的前提下，实现可控且具有语义意义的预测。实验结果表明，偏好监督作为一种经济高效的方法，可有效增强基于采样的生成模型。