NaviHydra: Controllable Navigation-guided End-to-end Autonomous Driving with Hydra-distillation
This addresses the need for controllable and safe autonomous driving systems, though it appears incremental as it builds on existing distillation and BEV methods.
The paper tackles the problem of autonomous driving models struggling to comply with explicit navigation commands in dynamic environments, presenting NaviHydra, which achieves state-of-the-art results in the NAVSIM benchmark.
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.