Active Interaction-Aware Model Predictive Path Integral via Ego-Conditioned Generative Predictions
arXiv:2608.21400v1 Announce Type: new Abstract: Dense traffic is inherently interactive. The ego vehicle and surrounding agents continuously influence each other's reactions, making "what-if" reasoning essential for safe and efficient driving. To enable such an active interaction-aware behavior, we propose a planning framework that integrates an ego-conditioned generative autoregressive prediction model within Model Predictive Path Integral (MPPI) control. The generative prediction model outputs stochastic, multi-modal predictions of surrounding agents conditioned on each of the ego's considered future actions. A nested sampling scheme enables tractable evaluation of expected cost and collision risk under the induced distribution. This formulation allows the ego to actively probe how different candidate actions shape the interaction outcomes and to identify actions that reduce ambiguity in uncertain interactions. Closed-loop simulations demonstrate improved safety and efficiency compared to conventional predict-then-plan and passive interaction-aware approaches.
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[Submitted on 6 Aug 2026]
Title:Active Interaction-Aware Model Predictive Path Integral via Ego-Conditioned Generative Predictions
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Abstract:Dense traffic is inherently interactive. The ego vehicle and surrounding agents continuously influence each other's reactions, making "what-if" reasoning essential for safe and efficient driving. To enable such an active interaction-aware behavior, we propose a planning framework that integrates an ego-conditioned generative autoregressive prediction model within Model Predictive Path Integral (MPPI) control. The generative prediction model outputs stochastic, multi-modal predictions of surrounding agents conditioned on each of the ego's considered future actions. A nested sampling scheme enables tractable evaluation of expected cost and collision risk under the induced distribution. This formulation allows the ego to actively probe how different candidate actions shape the interaction outcomes and to identify actions that reduce ambiguity in uncertain interactions. Closed-loop simulations demonstrate improved safety and efficiency compared to conventional predict-then-plan and passive interaction-aware approaches.
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Robotics (cs.RO)
Cite as: arXiv:2608.21400 [cs.RO]
(or arXiv:2608.21400v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.21400
arXiv-issued DOI via DataCite
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From: Mohamed-Khalil Bouzidi [view email] [v1] Thu, 6 Aug 2026 22:51:26 UTC (2,031 KB)
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