ProDrive：通过自我环境共演实现自动驾驶的前瞻性规划
ProDrive: Proactive Planning for Autonomous Driving via Ego-Environment Co-Evolution
AI总结：
ProDrive通过自我环境共演实现自动驾驶前瞻性规划，结合查询导向的轨迹规划器和鸟瞰图世界模型，提升安全性和规划效率。
摘要：
端到端自动驾驶规划器通常仅基于当前观测生成轨迹。然而，现实驾驶高度动态，此类反应式规划无法预测未来场景演变，常导致短视决策和安全关键失败。我们提出ProDrive，一种基于世界模型的前瞻性规划框架，实现自动驾驶的自我环境共演。ProDrive端到端联合训练查询导向的轨迹规划器和鸟瞰图（BEV）世界模型：规划器生成多样化的候选轨迹和规划感知的自我令牌，而世界模型基于这些令牌预测未来场景演变。通过将规划器特征注入世界模型并并行评估所有候选方案，ProDrive保持端到端梯度流动，使未来结果评估能够直接塑造规划。这种双向耦合使规划超越了基于当前观测的决策。在NAVSIM v1上的实验表明，ProDrive在安全性和规划效率上均优于强基线模型，消融实验验证了所提自我环境耦合设计的有效性。
英文摘要：
End-to-end autonomous driving planners typically generate trajectories from current observations alone. However, real-world driving is highly dynamic, and such reactive planning cannot anticipate future scene evolution, often leading to myopic decisions and safety-critical failures. We propose ProDrive, a world-model-based proactive planning framework that enables ego-environment co-evolution for autonomous driving. ProDrive jointly trains a query-centric trajectory planner and a bird's-eye-view (BEV) world model end-to-end: the planner generates diverse candidate trajectories and planning-aware ego tokens, while the world model predicts future scene evolution conditioned on them. By injecting planner features into the world model and evaluating all candidates in parallel, ProDrive preserves end-to-end gradient flow and allows future outcome assessment to directly shape planning. This bidirectional coupling enables proactive planning beyond current-observation-driven decision-making. Experiments on NAVSIM v1 show that ProDrive outperforms strong baselines in both safety and planning efficiency, while ablations validate the effectiveness of the proposed ego-environment coupling design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
