TwinFlux: One-Step Discrete-Continuous Flow for End-to-End Autonomous Driving

End-to-end autonomous driving planning requires trajectories that are multimodal, scene-compliant, and efficient to generate. Deterministic regression is fast but often collapses to a single future, while trajectory-vocabulary methods depend on predefined candidate coverage and iterative generative planners incur multi-step inference cost. We propose TwinFlux, a one-step discrete-continuous trajectory generation framework based on SplitMeanFlow. TwinFlux starts from data-driven trajectory prototypes and refines them through parallel heatmap and offset branches. The heatmap branch provides topology-aware coarse localization on a BEV grid, while the offset branch predicts grid-relative geometric refinement. A differentiable trajectory decoupling-and-reconstruction module connects this representation with physical trajectories. During training, TwinFlux enforces piecewise displacement consistency in reconstructed trajectory space, aligning direct one-step prediction with accumulated sub-interval evolution without Jacobian-vector-product computation or inference overhead. Experiments on NAVSIMv1 and NAVSIMv2 show that TwinFlux outperforms recent end-to-end planning baselines while maintaining real-time inference.