Reasonable Motion: A General ASP Foundation for Environment Constrained Movement Trajectory Computation
For researchers in autonomous driving and dynamic domains, this method offers a hybrid quantitative-qualitative approach with interpretable trajectories, though it is an incremental contribution combining existing techniques.
The paper presents a general answer set programming based hybrid method for computing constrained branching trajectory modes for moving objects, demonstrating applicability on the Argoverse 2 autonomous driving benchmark with verifiable interpretability.
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.