[2511.13306] DAP: A Discrete-token Autoregressive Planner for Autonomous Driving

Computer Science > Artificial Intelligence arXiv:2511.13306 (cs) [Submitted on 17 Nov 2025 (v1), last revised 5 Mar 2026 (this version, v2)] Title:DAP: A Discrete-token Autoregressive Planner for Autonomous Driving Authors:Bowen Ye, Bin Zhang, Hang Zhao View a PDF of the paper titled DAP: A Discrete-token Autoregressive Planner for Autonomous Driving, by Bowen Ye and 2 other authors View PDF HTML (experimental) 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...