用偏好标注控制轨迹生成多样性，提升自动驾驶安全规划
Controllable Generative Trajectory Prediction via Weak Preference Alignment
- 通过弱标签偏好对引导隐变量，实现语义可控的轨迹生成
- 在不降低准确率的前提下，实现平均速度可控的多样化预测
- 适合需要可解释、可调控生成结果的研究与工程场景
条件变分自编码器（CVAE）在自动驾驶中周围目标轨迹预测方面展现出巨大潜力。当前先进模型在预测精度上已取得显著进展。除了准确性，多样性对安全规划同样重要，因为人类行为具有固有的不确定性和多模态特征。然而，现有方法普遍缺乏可控多样性的生成机制，随机多样化不如语义可控的多样化实用。为此，我们提出PrefCVAE，一种增强型CVAE框架，利用弱标签偏好对为隐变量赋予语义属性。以平均速度为例，我们证明PrefCVAE可在不牺牲基线准确率的情况下，实现语义明确的可控预测。结果表明，偏好监督是一种低成本提升采样生成模型性能的有效方式。
原文摘要 · Abstract (English)
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.
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