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.

翻译：与其他交通参与者安全地互动是自主驾驶的核心要求之一,特别是在交叉点和隔离点。大多数现有办法都是针对特定情况设计的,需要大量人力来调整参数,以适用于不同情况。为了解决这个问题,我们首先提出一个基于学习的互动点模型(IPM),该模型以统一的方式描述代理人与保护时间和互动优先事项之间的互动。我们进一步将拟议的IPM纳入一个新的规划框架,通过在高度动态的环境中进行全面模拟,表明其有效性和稳健性。