Reasonable Motion: A General ASP Foundation for Environment Constrained Movement Trajectory Computation

2026-06-24

AI Summary

This work addresses the challenge of generating constrained, interpretable, and domain-compliant trajectory patterns for moving objects in real-world dynamic environments. It proposes a hybrid qualitative-quantitative approach based on Answer Set Programming (ASP), which traverses the environmental graph structure and integrates geometric constraint reasoning with stable model semantics to enumerate geometrically feasible motion behaviors. To the best of our knowledge, this is the first application of ASP to generate diverse trajectory patterns that are verifiable, traceable, and seamlessly incorporate domain knowledge with environmental topology. Experiments on the large-scale Argoverse 2 autonomous driving benchmark demonstrate that the generated trajectories exhibit high interpretability and practical applicability, effectively overcoming the limited explainability inherent in purely data-driven methods.

Abstract

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.

Problem

• constrained trajectory computation
• environment-constrained movement
• motion planning
• autonomous driving
• trajectory interpretability

Innovation

• Answer Set Programming
• constrained trajectory computation
• hybrid qualitative-quantitative reasoning
• verifiable interpretability
• autonomous driving