DAP: A Discrete-token Autoregressive Planner for Autonomous Driving

• URL: http://arxiv.org/abs/2511.13306v1
• Date: Mon, 17 Nov 2025 12:31:33 GMT
• Title: DAP: A Discrete-token Autoregressive Planner for Autonomous Driving
• Authors: Bowen Ye, Bin Zhang, Hang Zhao,
• Abstract summary: We introduce DAP, a discrete-token autoregressive planner that jointly forecasts BEV semantics and ego trajectories.<n>We incorporate a reinforcement-learning-based fine-tuning, which preserves supervised behavior cloning priors while injecting reward-guided improvements.<n>DAP achieves state-of-the-art performance on open-loop metrics and delivers competitive closed-loop results on the NAVSIM benchmark.
• Score: 34.32497598431514
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gaining sustainable performance improvement with scaling data and model budget remains a pivotal yet unresolved challenge in autonomous driving. While autoregressive models exhibited promising data-scaling efficiency in planning tasks, predicting ego trajectories alone suffers sparse supervision and weakly constrains how scene evolution should shape ego motion. Therefore, we introduce DAP, a discrete-token autoregressive planner that jointly forecasts BEV semantics and ego trajectories, thereby enforcing comprehensive representation learning and allowing predicted dynamics to directly condition ego motion. In addition, we incorporate a reinforcement-learning-based fine-tuning, which preserves supervised behavior cloning priors while injecting reward-guided improvements. Despite a compact 160M parameter budget, DAP achieves state-of-the-art performance on open-loop metrics and delivers competitive closed-loop results on the NAVSIM benchmark. Overall, the fully discrete-token autoregressive formulation operating on both rasterized BEV and ego actions provides a compact yet scalable planning paradigm for autonomous driving.

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