Risk-Aware Rulebooks for Multi-Objective Trajectory Evaluation under Uncertainty
For autonomous systems like self-driving cars, it provides a principled way to handle uncertain interactions and complex objective hierarchies in trajectory evaluation.
This paper introduces a risk-aware rulebook formalism for multi-objective trajectory evaluation under uncertainty, explicitly modeling how system trajectories influence environment responses. The approach ensures consistent, non-cyclic preferences and is demonstrated on an autonomous driving example to improve explainability.
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.