# Minimum-Violation Planning for Autonomous Systems: Theoretical and Practical Considerations

arXiv: https://arxiv.org/abs/2009.11954 · HTML: https://mlsanity.com/paper/2009.11954/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications](https://mlsanity.com/paper/2210.01162/) (2022)
- [Probabilistic Planning with Partially Ordered Preferences over Temporal Goals](https://mlsanity.com/paper/2209.12267/) (2022)
- [Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives](https://mlsanity.com/paper/2304.11641/) (2023)
- [Opportunistic Qualitative Planning in Stochastic Systems with Incomplete Preferences over Reachability Objectives](https://mlsanity.com/paper/2210.01878/) (2022)
- [Preference-Based Planning in Stochastic Environments: From Partially-Ordered Temporal Goals to Most Preferred Policies](https://mlsanity.com/paper/2403.18212/) (2024)
- [Incorporating System-level Safety Requirements in Perception Models via Reinforcement Learning](https://mlsanity.com/paper/2412.02951/) (2024)
- [Automata Learning of Preferences over Temporal Logic Formulas from Pairwise Comparisons](https://mlsanity.com/paper/2505.18030/) (2025)

## Abstract (arXiv)

This paper considers the problem of computing an optimal trajectory for an autonomous system that is subject to a set of potentially conflicting rules. First, we introduce the concept of prioritized safety specifications, where each rule is expressed as a temporal logic formula with its associated weight and priority. The optimality is defined based on the violation of such prioritized safety specifications. We then introduce a class of temporal logic formulas called textrmsi-FLTL_mathsfGX and develop an efficient, incremental sampling-based approach to solve this minimum-violation planning problem with guarantees on asymptotic optimality. We illustrate the application of the proposed approach in autonomous vehicles, showing that textrmsi-FLTL_mathsfGX formulas are sufficiently expressive to describe many traffic rules. Finally, we discuss practical considerations and present simulation results for a vehicle overtaking scenario.