NaviHydra: Controllable Navigation-guided End-to-end Autonomous Driving with Hydra-distillation

• URL: http://arxiv.org/abs/2512.10660v1
• Date: Thu, 11 Dec 2025 14:05:18 GMT
• Title: NaviHydra: Controllable Navigation-guided End-to-end Autonomous Driving with Hydra-distillation
• Authors: Hanfeng Wu, Marlon Steiner, Michael Schmidt, Alvaro Marcos-Ramiro, Christoph Stiller,
• Abstract summary: NaviHydra is a controllable navigation-guided end-to-end model distilled from an existing rule-based simulator.<n>Our framework accepts high-level navigation commands as control signals, generating trajectories that align with specified intentions.
• Score: 6.722962084265695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The complexity of autonomous driving scenarios requires robust models that can interpret high-level navigation commands and generate safe trajectories. While traditional rule-based systems can react to these commands, they often struggle in dynamic environments, and end-to-end methods face challenges in complying with explicit navigation commands. To address this, we present NaviHydra, a controllable navigation-guided end-to-end model distilled from an existing rule-based simulator. Our framework accepts high-level navigation commands as control signals, generating trajectories that align with specified intentions. We utilize a Bird's Eye View (BEV) based trajectory gathering method to enhance the trajectory feature extraction. Additionally, we introduce a novel navigation compliance metric to evaluate adherence to intended route, improving controllability and navigation safety. To comprehensively assess our model's controllability, we design a test that evaluates its response to various navigation commands. Our method significantly outperforms baseline models, achieving state-of-the-art results in the NAVSIM benchmark, demonstrating its effectiveness in advancing autonomous driving.

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