Precisely predicting the future trajectories of surrounding traffic participants is a crucial but challenging problem in autonomous driving, due to complex interactions between traffic agents, map context and traffic rules. Vector-based approaches have recently shown to achieve among the best performances on trajectory prediction benchmarks. These methods model simple interactions between traffic agents but don't distinguish between relation-type and attributes like their distance along the road. Furthermore, they represent lanes only by sequences of vectors representing center lines and ignore context information like lane dividers and other road elements. We present a novel approach for vector-based trajectory prediction that addresses these shortcomings by leveraging three crucial sources of information: First, we model interactions between traffic agents by a semantic scene graph, that accounts for the nature and important features of their relation. Second, we extract agent-centric image-based map features to model the local map context. Finally, we generate anchor paths to enforce the policy in multi-modal prediction to permitted trajectories only. Each of these three enhancements shows advantages over the baseline model HoliGraph.

翻译：精准预测周围交通参与者的未来轨迹是自动驾驶中至关重要但极具挑战性的问题，这源于交通参与者、地图上下文及交通规则间复杂的交互关系。近期基于向量的方法在轨迹预测基准测试中展现出最优性能。这类方法虽能建模交通参与者间的简单交互，但未能区分关系类型及沿道路距离等属性特征；同时，其仅用表示车道中心线的向量序列来表征车道，忽略了车道分隔线及其他道路元素等上下文信息。我们提出一种全新的基于向量的轨迹预测方法，通过利用三类关键信息弥补上述不足：第一，利用语义场景图建模交通参与者间的交互，该图能够反映关系性质及关键特征；第二，提取以参与者为中心的图像化地图特征，建模局部地图上下文；第三，生成锚定路径以约束多模态预测中的策略，使其仅输出合法轨迹。这三项增强机制在基线模型HoliGraph基础上均展现出显著优势。