Risk-Aware Rulebooks for Multi-Objective Trajectory Evaluation under Uncertainty
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arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917436060598272 |
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| author | Wongpiromsarn, Tichakorn |
| author_facet | Wongpiromsarn, Tichakorn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04603 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Risk-Aware Rulebooks for Multi-Objective Trajectory Evaluation under Uncertainty Wongpiromsarn, Tichakorn Systems and Control Robotics 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. |
| title | Risk-Aware Rulebooks for Multi-Objective Trajectory Evaluation under Uncertainty |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2603.04603 |