aiqbench analysis
This research advances safety-critical control by integrating Signal Temporal Logic (STL) with Model Predictive Control (MPC) to handle stochastic, uncontrollable agents in multi-agent systems like autonomous driving. By framing feasibility repair as a Pareto optimization problem, the framework explicitly balances competing agent objectives while providing probabilistic certificates for constraint violations under uncertainty. The approach offers a rigorous method for maintaining safe behaviors when strict STL satisfaction is impossible due to environmental unpredictability.
Scenario MPC with STL Specifications and Pareto-Based Feasibility Repair
arXiv:2609.23263v1 Announce Type: new Abstract: Temporal logic is a formal language for reasoning about system behaviors over time. Signal temporal logic (STL), in particular, has been used to encode spatio-temporal requirements for control synthesis in multi-agent systems, often under the assumption that agents are cooperative and their dynamics are known. However, real-world multi-agent applications, such as autonomous driving, typically involve stochastic and uncontrollable agents. Recent work explored robust control with worst-case or probabilistic formulations, but remains limited in that it either (1) certifies strict satisfaction of STL constraints without addressing feasibility recovery, or (2) relaxes infeasible constraints with ego-centric objectives. In this paper, we propose a model predictive control (MPC) framework that treats feasibility repair as a Pareto optimization problem to explicitly characterize tradeoffs among agent objectives. We further provide a probabilistic certificate on STL violation rate to formally quantify uncertainty under stochastic and uncontrollable agents. The proposed framework is evaluated on two autonomous driving scenarios. Results show that the framework recovers feasible control with demonstrated safe behaviors.
Abstract:Temporal logic is a formal language for reasoning about system behaviors over time. Signal temporal logic (STL), in particular, has been used to encode spatio-temporal requirements for control synthesis in multi-agent systems, often under the assumption that agents are cooperative and their dynamics are known. However, real-world multi-agent applications, such as autonomous driving, typically involve stochastic and uncontrollable agents. Recent work explored robust control with worst-case or probabilistic formulations, but remains limited in that it either (1) certifies strict satisfaction of STL constraints without addressing feasibility recovery, or (2) relaxes infeasible constraints with ego-centric objectives. In this paper, we propose a model predictive control (MPC) framework that treats feasibility repair as a Pareto optimization problem to explicitly characterize tradeoffs among agent objectives. We further provide a probabilistic certificate on STL violation rate to formally quantify uncertainty under stochastic and uncontrollable agents. The proposed framework is evaluated on two autonomous driving scenarios. Results show that the framework recovers feasible control with demonstrated safe behaviors.
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2609.23263 [cs.RO]
(or arXiv:2609.23263v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.23263 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 260 words
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