Planning safe trajectories for autonomous vehicles is essential for operational safety but remains extremely challenging due to the complex interactions among traffic participants. Recent autonomous driving frameworks have focused on improving prediction accuracy to explicitly model these interactions. However, some methods overlook the significant influence of the ego vehicle's planning on the possible trajectories of other agents, which can alter prediction accuracy and lead to unsafe planning decisions. In this paper, we propose a novel motion Planning approach by Simulation with learning-based parallel scenario prediction (PS). PS deduces predictions iteratively based on Monte Carlo Tree Search (MCTS), jointly inferring scenarios that cooperate with the ego vehicle's planning set. Our method simulates possible scenes and calculates their costs after the ego vehicle executes potential actions. To balance and prune unreasonable actions and scenarios, we adopt MCTS as the foundation to explore possible future interactions encoded within the prediction network. Moreover, the query-centric trajectory prediction streamlines our scene generation, enabling a sophisticated framework that captures the mutual influence between other agents' predictions and the ego vehicle's planning. We evaluate our framework on the Argoverse 2 dataset, and the results demonstrate that our approach effectively achieves parallel ego vehicle planning.

翻译：为自动驾驶车辆规划安全轨迹对于运行安全至关重要，但由于交通参与者之间复杂的交互作用，这仍然极具挑战性。近期的自动驾驶框架侧重于提高预测精度，以显式地建模这些交互。然而，一些方法忽略了自车规划对其他智能体可能轨迹的显著影响，这种影响会改变预测精度并导致不安全的规划决策。本文提出了一种新颖的基于仿真的运动规划方法，该方法结合了基于学习的并行场景预测（PS）。PS基于蒙特卡洛树搜索（MCTS）进行迭代推理预测，联合推断与自车规划集协作的场景。我们的方法模拟可能的场景，并在自车执行潜在动作后计算其代价。为了平衡并剪枝不合理的动作和场景，我们采用MCTS作为基础，以探索编码在预测网络内的未来可能交互。此外，以查询为中心的轨迹预测简化了我们的场景生成，从而形成了一个能够捕捉其他智能体预测与自车规划之间相互影响的复杂框架。我们在Argoverse 2数据集上评估了我们的框架，结果表明我们的方法有效地实现了自车的并行规划。