让自动驾驶更自然：用多凸集模型捕捉行人与车辆的多样行为模式。
Act Natural! Extending Naturalistic Projection to Multimodal Behavior Scenarios
- 用多个凸集表示人类行为的多种可能，提升自然性建模灵活性。
- 在inD和rounD数据集上验证，能有效将任意轨迹投影为自然行为。
- 适合需要拟人化决策的自动驾驶系统开发者使用。
在公共空间中运行的自主代理需考虑其行为对周围人类的影响，即使未直接互动。为此，保持可预测性和自然性至关重要。现有方法依赖人类意图建模或模仿学习，但往往无法覆盖所有人类行为动机，且需大量数据。本文将单模态自然行为的显式凸集表示扩展至多模态场景，采用多个凸集建模复杂行为，如环岛处是否让行。该方法显著提升了真实场景中离散行为（如是否让行）的数据驱动建模精度。基于此，我们设计了一种优化过滤器，将任意轨迹投影至多凸集，使其在满足车辆动力学和执行器限制的前提下，呈现出自然的人类行为特征。在inD（交叉口）和rounD（环岛）的真实驾驶数据集上进行了验证。
原文摘要 · Abstract (English)
Autonomous agents operating in public spaces must consider how their behaviors might affect the humans around them, even when not directly interacting with them. To this end, it is often beneficial to be predictable and appear naturalistic. Existing methods for this purpose use human actor intent modeling or imitation learning techniques, but these approaches rarely capture all possible motivations for human behavior and/or require significant amounts of data. Our work extends a technique for modeling unimodal naturalistic behaviors with an explicit convex set representation, to account for multimodal behavior by using multiple convex sets. This more flexible representation provides a higher degree of fidelity in data-driven modeling of naturalistic behavior that arises in real-world scenarios in which human behavior is, in some sense, discrete, e.g. whether or not to yield at a roundabout. Equipped with this new set representation, we develop an optimization-based filter to project arbitrary trajectories into the set so that they appear naturalistic to humans in the scene, while also satisfying vehicle dynamics, actuator limits, etc. We demonstrate our methods on real-world human driving data from the inD (intersection) and rounD (roundabout) datasets.
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