Addressing the Waypoint-Action Gap in End-to-End Autonomous Driving via Vehicle Motion Models

• URL: http://arxiv.org/abs/2602.06214v2
• Date: Mon, 09 Feb 2026 11:33:20 GMT
• Title: Addressing the Waypoint-Action Gap in End-to-End Autonomous Driving via Vehicle Motion Models
• Authors: Jorge Daniel Rodríguez-Vidal, Gabriel Villalonga, Diego Porres, Antonio M. López Peña,
• Abstract summary: We propose a differentiable vehicle-model framework that rolls out predicted action sequences to their corresponding ego-frame waypoint trajectories.<n>Our approach enables action-based architectures to be trained and evaluated, for the first time, within waypoint-based benchmarks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: End-to-End Autonomous Driving (E2E-AD) systems are typically grouped by the nature of their outputs: (i) waypoint-based models that predict a future trajectory, and (ii) action-based models that directly output throttle, steer and brake. Most recent benchmark protocols and training pipelines are waypoint-based, which makes action-based policies harder to train and compare, slowing their progress. To bridge this waypoint-action gap, we propose a novel, differentiable vehicle-model framework that rolls out predicted action sequences to their corresponding ego-frame waypoint trajectories while supervising in waypoint space. Our approach enables action-based architectures to be trained and evaluated, for the first time, within waypoint-based benchmarks without modifying the underlying evaluation protocol. We extensively evaluate our framework across multiple challenging benchmarks and observe consistent improvements over the baselines. In particular, on NAVSIM \texttt{navhard} our approach achieves state-of-the-art performance. Our code will be made publicly available upon acceptance.

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