Abstract
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.
Abstract (translated)
规划自动驾驶车辆的安全轨迹对于操作安全性至关重要，但由于交通参与者之间的复杂交互作用，这一过程仍然极具挑战性。最近的自主驾驶框架专注于提高预测准确性以明确建模这些交互作用。然而，一些方法忽视了自车规划对其他代理可能轨迹的重大影响，这可能会改变预测准确性并导致不安全的规划决策。在本文中，我们提出了一种基于仿真与学习型平行场景预测（PS）的新运动规划方法。PS 基于蒙特卡洛树搜索（MCTS），迭代地推导出与自车规划集合作用的场景推测。我们的方法模拟可能的情景，并计算自车执行潜在动作后的成本。为了平衡和修剪不合理的动作和情景，我们采用 MCTS 作为基础来探索预测网络中编码的可能未来交互作用。此外，以查询为中心的轨迹预测简化了我们的场景生成过程，使我们能够构建一个复杂的框架，捕捉其他代理预测与自车规划之间的相互影响。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平行的自车规划。
URL
https://arxiv.org/abs/2411.09887
