Risk-Aware Rulebooks for Multi-Objective Trajectory Evaluation under Uncertainty

TL;DR

This paper introduces a formalism for evaluating system trajectories in uncertain environments, emphasizing risk-awareness, hierarchical objectives, and explainability, demonstrated through autonomous driving examples.

Contribution

It develops a novel risk-aware formalism that models environment interactions explicitly and ensures consistent trajectory evaluation with hierarchical and complex requirements.

Findings

Formalism induces a preorder on trajectories ensuring consistency.

Explicit environment modeling improves trajectory evaluation.

Application to autonomous driving enhances explainability.

Abstract

We present a risk-aware formalism for evaluating system trajectories in the presence of uncertain interactions between the system and its environment. The proposed formalism supports reasoning under uncertainty and systematically handles complex relationships among requirements and objectives, including hierarchical priorities and non-comparability. Rather than treating the environment as exogenous noise, we explicitly model how each system trajectory influences the environment and evaluate trajectories under the resulting distribution of environment responses. We prove that the formalism induces a preorder on the set of system trajectories, ensuring consistency and preventing cyclic preferences. Finally, we illustrate the approach with an autonomous driving example that demonstrates how the formalism enhances explainability by clarifying the rationale behind trajectory selection.