Normalizing Flows are Capable Trajectory Planners

Generative models such as Diffusion Models and Flow Matching have improved multimodal trajectory planning in autonomous driving, but still suffer from inference latency and the lack of tractable probability densities, relying on heuristic trajectory selection. We propose BiDrive, a real-time end-to-end planning framework based on Normalizing Flows (NFs). By leveraging invertibility and exact likelihood estimation, BiDrive directly evaluates trajectory probabilities in a single forward pass, enabling principled maximum-likelihood-based decision making. To address the autoregressive bottleneck of expressive flows, we further develop a bidirectional distillation framework that compresses an autoregressive flow into a one-pass generator, achieving real-time inference (50 FPS) while preserving modeling capacity. The autoregressive model is used only during training as a probabilistic teacher. In addition, we introduce a latent-space optimization mechanism that incorporates differentiable safety and dynamic constraints for efficient test-time trajectory refinement. Experiments on NAVSIM closed-loop benchmarks demonstrate state-of-the-art performance in both safety and efficiency, highlighting the potential of Normalizing Flows for real-time trajectory planning. Code is available at: https://anonymous.4open.science/r/BiDrive.