Goal-Oriented Reactive Simulation for Closed-Loop Trajectory Prediction

• URL: http://arxiv.org/abs/2603.24155v1
• Date: Wed, 25 Mar 2026 10:24:16 GMT
• Title: Goal-Oriented Reactive Simulation for Closed-Loop Trajectory Prediction
• Abstract summary: Current trajectory prediction models are primarily trained in an open-loop manner.<n>We propose an on-policy closed-loop training paradigm optimized for high-frequency, receding horizon ego prediction.
• Score: 9.812666469580872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current trajectory prediction models are primarily trained in an open-loop manner, which often leads to covariate shift and compounding errors when deployed in real-world, closed-loop settings. Furthermore, relying on static datasets or non-reactive log-replay simulators severs the interactive loop, preventing the ego agent from learning to actively negotiate surrounding traffic. In this work, we propose an on-policy closed-loop training paradigm optimized for high-frequency, receding horizon ego prediction. To ground the ego prediction in a realistic representation of traffic interactions and to achieve reactive consistency, we introduce a goal-oriented, transformer-based scene decoder, resulting in an inherently reactive training simulation. By exposing the ego agent to a mixture of open-loop data and simulated, self-induced states, the model learns recovery behaviors to correct its own execution errors. Extensive evaluation demonstrates that closed-loop training significantly enhances collision avoidance capabilities at high replanning frequencies, yielding relative collision rate reductions of up to 27.0% on nuScenes and 79.5% in dense DeepScenario intersections compared to open-loop baselines. Additionally, we show that a hybrid simulation combining reactive with non-reactive surrounding agents achieves optimal balance between immediate interactivity and long-term behavioral stability.

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