用离散令牌联合预测场景语义与车辆轨迹，提升自动驾驶规划性能。
DAP: A Discrete-token Autoregressive Planner for Autonomous Driving
- 采用离散令牌的自回归框架，同步预测鸟瞰图语义与车辆轨迹。
- 160M参数下在开放环评测中达顶尖水平，闭环测试表现优异。
- 结合强化学习微调，兼顾监督学习先验与奖励驱动优化。
在自动驾驶领域，持续提升性能同时扩大数据与模型规模仍是核心挑战。尽管自回归模型在规划任务中展现出良好的数据扩展效率，但仅预测自身轨迹存在监督稀疏问题，难以有效约束场景演化对自身运动的影响。为此，我们提出DAP——一种离散令牌自回归规划器，联合预测鸟瞰图（BEV）语义与自车轨迹，实现全面表征学习，并使预测动态直接指导自车运动。此外，引入基于强化学习的微调策略，在保留监督行为克隆先验的基础上注入奖励引导改进。尽管模型参数量仅为160M，DAP在开放环指标上达到业界领先水平，并在NAVSIM基准上取得具有竞争力的闭环表现。整体而言，该基于栅格化BEV和自车动作的全离散令牌自回归范式，为自动驾驶规划提供了一个紧凑且可扩展的新方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。