用逻辑编程计算受限环境下移动物体的合理轨迹模式。
Reasonable Motion: A General ASP Foundation for Environment Constrained Movement Trajectory Computation
- 基于答案集编程，结合定量与定性分析生成轨迹。
- 在Argoverse 2上验证，轨迹可追溯至稳定模型。
- 适合需要可解释性的自动驾驶等场景。
我们提出一种基于答案集编程的混合定量-定性方法，用于计算现实环境中移动对象的受限分支轨迹模式。该方法在环境图上进行约束遍历，将几何可行的运动行为枚举为稳定模型，每个模型构成一种由领域相关与无关因素（如事件序列、地图拓扑、领域规范）共同定义的轨迹模式。该混合轨迹计算方法普遍适用于各类动态领域中常见的运动特征，例如自动驾驶。我们通过实证评估验证了其适用性，并展示了计算出的轨迹可追溯至其底层稳定模型，从而提供纯学习方法无法实现的可验证可解释性。评估基于大规模真实世界自动驾驶基准Argoverse 2，代表了本方法涵盖的动态领域类别。
原文摘要 · Abstract (English)
We present a general answer set programming based hybrid quantitative-qualitative method for computing constrained branching trajectory modes for moving objects in real-world settings. The method performs constrained traversal of an environment graph, enumerating geometrically admissible motion behaviours as stable models, each constituting a distinct trajectory mode characterised by both domain-dependent and independent factors such as derived event sequence, map topology, and domain norms. The hybrid trajectory computation method is generally applicable across motion characteristics typically encountered in diverse dynamic domains with moving objects, e.g., autonomous driving. We demonstrate applicability and highlight how computed trajectories are traceable to their underlying stable model, thereby affording verifiable interpretability that purely learned approaches cannot provide. We also perform an empirical evaluation with Argoverse 2, a large-scale real-world autonomous driving benchmark representative of the class of dynamic domains within the scope of the proposed method.
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