STL仕様とパレート最適化に基づく実現可能性修復を備えたシナリオMPC
Scenario MPC with STL Specifications and Pareto-Based Feasibility Repair
確率的で制御不能なエージェントが存在するマルチエージェント系において、STL制約の実現可能性修復をパレート最適化として扱うMPCフレームワークを提案し、自動運転シナリオで評価した。
詳しい要約
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著者: Tianhao Wu, Yiwei Lyu
分類: cs.RO, eess.SY
原文アブストラクト
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.
