Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning

• URL: http://arxiv.org/abs/2606.08136v1
• Date: Sat, 06 Jun 2026 12:26:21 GMT
• Title: Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning
• Abstract summary: Model Predictive Control (MPC) is widely used for autonomous-vehicle (AV) motion planning, but its real-time applicability is often limited.<n>Actor-critic offers an online solution for nonlinear learning problems in dynamic road environments.<n>This article proposes a framework with deep Koopman operators for efficient real-time motion control.
• Score: 11.149068950974113
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Model Predictive Control (MPC) is widely used for autonomous-vehicle (AV) motion planning, but its real-time applicability is often limited by the need for accurate models and online solution of nonlinear, nonconvex optimization problems in dynamic road environments. Actor-critic reinforcement learning offers a promising alternative for online policy generation, yet its policy-learning process often lacks explicit control-theoretic structure. This article proposes a learning predictive control (LPC) framework with deep Koopman operators for efficient real-time motion planning under nonconvex constraints. To address nonlinear and uncertain vehicle dynamics, a deep-Koopman-based predictor is used to lift the system into an interpretable linear observable space in a data-driven manner. Unlike traditional MPC, which computes open-loop control sequences, the proposed LPC framework yields a closed-loop state-feedback policy within each prediction interval through receding-horizon actor-critic learning. To ensure safety under nonconvex environmental constraints, LPC constructs convex local surrogate representations of obstacles and defines corresponding potential-field functions. These functions and their gradients are directly embedded into the actor-critic structure, enabling efficient, safety-aware policy learning. Extensive simulations and real-world experiments on the HongQi-EHS3 platform demonstrate favorable performance in diverse obstacle-avoidance scenarios in terms of safety, computational efficiency, and driving comfort, compared with benchmark methods such as CBF-MPC and LMPCC.

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