Closing the Navigation Compliance Gap in End-to-end Autonomous Driving

TL;DR

This paper introduces new metrics and a dataset to improve navigation compliance in autonomous driving, addressing the gap between command adherence and trajectory planning at intersections.

Contribution

It presents NAVI and CM metrics, a large dataset NavControl, and a trajectory-scoring planner NaviHydra that significantly enhances navigation compliance.

Findings

NAVI achieves 92.7 PDM score on NAVSIM navtest split.

NavControl dataset improves controllability across architectures.

NaviHydra outperforms existing methods in navigation compliance.

Abstract

Trajectory-scoring planners achieve high navigation compliance when following the expert's original command, yet they struggle at intersections when presented with alternative commands; over 30 percent of such commands are ignored. We attribute this navigation compliance gap to two root causes: (1) existing metrics like Ego Progress do not explicitly measure navigation adherence, diluting the gap between on-route and off-route trajectories; and (2) current datasets pair each scenario with a single command, preventing models from learning command-dependent behavior. We address the metric gap by introducing the binary Navigation Compliance metric (NAVI) and the derived Controllability Measure (CM), and the data gap with the NavControl dataset, 14,918 intersection scenarios augmented with all feasible alternative commands and routing annotations, yielding over 34,000 direction samples. Building on these, we propose NaviHydra, a trajectory-scoring planner incorporating NAVI distillation and Bird's Eye View (BEV)-based trajectory gathering for context-position-aware trajectory feature extraction. NaviHydra achieves 92.7 PDM score on NAVSIM navtest split and 77.5 CM on NavControl test split. Training with NavControl improves controllability across diverse architectures, confirming it as a broadly effective augmentation for navigation compliance.