Predicting multiple trajectories for road users is important for automated driving systems: ego-vehicle motion planning indeed requires a clear view of the possible motions of the surrounding agents. However, the generative models used for multiple-trajectory forecasting suffer from a lack of diversity in their proposals. To avoid this form of collapse, we propose a novel method for structured prediction of diverse trajectories. To this end, we complement an underlying pretrained generative model with a diversity component, based on a determinantal point process (DPP). We balance and structure this diversity with the inclusion of knowledge-based quality constraints, independent from the underlying generative model. We combine these two novel components with a gating operation, ensuring that the predictions are both diverse and within the drivable area. We demonstrate on the nuScenes driving dataset the relevance of our compound approach, which yields significant improvements in the diversity and the quality of the generated trajectories.

翻译：预测道路使用者的多条轨迹对于自动驾驶系统至关重要：自车运动规划确实需要清晰了解周围智能体可能的运动方式。然而，用于多轨迹预测的生成模型在生成的轨迹提议中往往缺乏多样性。为避免这种模式坍缩，我们提出了一种新的结构化预测多样化轨迹的方法。为此，我们基于行列式点过程为底层预训练生成模型补充多样性组件。我们通过引入独立于底层生成模型的知识型质量约束来平衡并结构化这种多样性。我们将这两个新型组件与门控操作相结合，确保预测结果既具多样性，又位于可行驶区域内。我们在nuScenes驾驶数据集上验证了这种复合方法的相关性，该方法在生成轨迹的多样性和质量方面均取得了显著改进。