显式建模交互关系能显著提升轨迹联合分布学习效果
Studying the Effect of Explicit Interaction Representations on Learning Scene-level Distributions of Human Trajectories
- 在相同网络结构下对比显式与隐式交互建模方式
- 显式定义交互（如谁先通过路口）性能更优
- 适合自动驾驶中多智能体协同预测场景
准确捕捉场景中所有智能体的联合分布对预测场景真实演化至关重要，可为自动驾驶决策提供更精准信息。尽管近年已发展出多种模型，但如何最优表示智能体间交互仍无定论：是让神经网络从数据中隐式学习，还是基于空间时间关系显式建模？本文在同一网络结构下比较不同交互表示方法对最终联合分布学习的影响。结果表明，单纯依赖网络自动建立交互连接反而会损害性能；而明确定义交互（如一对智能体中谁先通过交叉口）通常能带来明显性能提升。
原文摘要 · Abstract (English)
Effectively capturing the joint distribution of all agents in a scene is relevant for predicting the true evolution of the scene and in turn providing more accurate information to the decision processes of autonomous vehicles. While new models have been developed for this purpose in recent years, it remains unclear how to best represent the joint distributions particularly from the perspective of the interactions between agents. Thus far there is no clear consensus on how best to represent interactions between agents; whether they should be learned implicitly from data by neural networks, or explicitly modeled using the spatial and temporal relations that are more grounded in human decision-making. This paper aims to study various means of describing interactions within the same network structure and their effect on the final learned joint distributions. Our findings show that more often than not, simply allowing a network to establish interactive connections between agents based on data has a detrimental effect on performance. Instead, having well defined interactions (such as which agent of an agent pair passes first at an intersection) can often bring about a clear boost in performance.
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