论文标题
在没有标签的统一概率模型中学习和预测多模式的车辆动作分布
Learning and Predicting Multimodal Vehicle Action Distributions in a Unified Probabilistic Model Without Labels
论文作者
论文摘要
我们提出了一个统一的概率模型，该模型学习了一组代表性的离散车辆动作，并预测给定特定情况的每个动作的概率。我们的模型还使我们能够估算在情况下的连续轨迹上的分布，这代表了在这种情况下执行的每个离散操作的外观。尽管我们的主要目标是学习代表性的作用集，但这些功能结合在一起，以产生准确的多模式轨迹预测作为副产品。尽管我们学到的动作表示形式与语义上有意义的类别非常相似（例如，“直接”，“左转”等），但我们的方法完全是自我保护的，并且不使用任何手动生成的标签或类别。我们的方法基于变化推理和深度无监督聚类的最新进展，从而基于确定性模型评估得出了完整的分布估计。
We present a unified probabilistic model that learns a representative set of discrete vehicle actions and predicts the probability of each action given a particular scenario. Our model also enables us to estimate the distribution over continuous trajectories conditioned on a scenario, representing what each discrete action would look like if executed in that scenario. While our primary objective is to learn representative action sets, these capabilities combine to produce accurate multimodal trajectory predictions as a byproduct. Although our learned action representations closely resemble semantically meaningful categories (e.g., "go straight", "turn left", etc.), our method is entirely self-supervised and does not utilize any manually generated labels or categories. Our method builds upon recent advances in variational inference and deep unsupervised clustering, resulting in full distribution estimates based on deterministic model evaluations.
