论文标题
通过交互点模型在动态环境中自动驾驶的有效速度计划
Efficient Speed Planning for Autonomous Driving in Dynamic Environment with Interaction Point Model
论文作者
论文摘要
安全与其他交通参与者的互动是自动驾驶的核心要求之一，尤其是在交叉点和遮挡中。大多数现有方法都是针对特定场景设计的，需要大量的人工劳动参数调整，以应用于不同情况。为了解决这个问题，我们首先提出了一个基于学习的交互点模型（IPM），该模型描述了代理与保护时间和交互优先级之间的相互作用以统一的方式。我们将提出的IPM进一步整合到一个新颖的计划框架中，并通过高度动态的环境中的全面模拟来证明其有效性和鲁棒性。
Safely interacting with other traffic participants is one of the core requirements for autonomous driving, especially in intersections and occlusions. Most existing approaches are designed for particular scenarios and require significant human labor in parameter tuning to be applied to different situations. To solve this problem, we first propose a learning-based Interaction Point Model (IPM), which describes the interaction between agents with the protection time and interaction priority in a unified manner. We further integrate the proposed IPM into a novel planning framework, demonstrating its effectiveness and robustness through comprehensive simulations in highly dynamic environments.
