Scenario Orchestration for Closed-loop Policy Evaluation of Autonomous Driving
Overview
This research introduces a scenario orchestrator designed for evaluating autonomous driving policies. The system continuously monitors traffic states and adapts non-ego actors through re-timing, re-routing, and role reassignment to maintain intended scenario interactions during closed-loop testing. By solving constrained optimization problems at each step, the method preserves interaction intent while limiting interventions and ensuring traffic feasibility. Experiments in the CARLA simulator demonstrate that this approach realizes targeted interactions more reliably than scripted or learned actor-level baselines, particularly under varying initial conditions and ego behaviors. The work supports closed-loop scenario orchestration as a tool for controlled and reproducible policy evaluation.
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Environment: simulation
“and maintaining traffic feasibility. We further distinguish scenario realization from policy failure, allowing a test to first be judged based on whether the intended interaction occurred and then by how the ego policy responded. Experiments in the CARLA simulator across multiple scenario families and driving policies show that orchestration realizes targeted interactions more reliably than scripted and learned actor”
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- arXiv Robotics — research abstractsSignalScenario Orchestration for Closed-loop Policy Evaluation of Autonomous Driving
This research introduces a scenario orchestrator designed for evaluating autonomous driving policies. The system continuously monitors traffic states and adapts non-ego actors through re-timing, re-routing, and role reassignment to maintain intended scenario interactions during closed-loop testing. By solving constrained optimization problems at each step, the method preserves interaction intent while limiting interventions and ensuring traffic feasibility. Experiments in the CARLA simulator demonstrate that this approach realizes targeted interactions more reliably than scripted or learned actor-level baselines, particularly under varying initial conditions and ego behaviors. The work supports closed-loop scenario orchestration as a tool for controlled and reproducible policy evaluation.
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