Prior arts in the field of motion predictions for autonomous driving tend to focus on finding a trajectory that is close to the ground truth trajectory. Such problem formulations and approaches, however, frequently lead to loss of diversity and biased trajectory predictions. Therefore, they are unsuitable for real-world autonomous driving where diverse and road-dependent multimodal trajectory predictions are critical for safety. To this end, this study proposes a novel loss function, \textit{Lane Loss}, that ensures map-adaptive diversity and accommodates geometric constraints. A two-stage trajectory prediction architecture with a novel trajectory candidate proposal module, \textit{Trajectory Prediction Attention (TPA)}, is trained with Lane Loss encourages multiple trajectories to be diversely distributed, covering feasible maneuvers in a map-aware manner. Furthermore, considering that the existing trajectory performance metrics are focusing on evaluating the accuracy based on the ground truth future trajectory, a quantitative evaluation metric is also suggested to evaluate the diversity of predicted multiple trajectories. The experiments performed on the Argoverse dataset show that the proposed method significantly improves the diversity of the predicted trajectories without sacrificing the prediction accuracy.
翻译:在自动驾驶运动预测领域,先前的艺术往往侧重于寻找接近地面真理轨道的轨迹。但是,这类问题公式和方法往往导致多样性的丧失和偏差的轨迹预测。因此,它们不适合现实世界自主驱动,因为多样化和依赖道路的多式联运轨迹预测对安全至关重要。为此,本研究提出一个新的损失函数,\textit{Lane Loss},以确保地图适应多样性并顾及几何限制。双阶段轨迹预测结构,配有新的轨迹候选建议模块,\textit{Trioty Poutical Convention attention (TPA),接受Lain Law Loss的训练,鼓励多种轨迹分布多样化,以地图认知方式涵盖可行的动作。此外,考虑到现有的轨迹性指标侧重于根据地面真理未来轨迹评估准确性,还提出了定量评价指标,以评价预测的多轨迹的多样性。在Argoverset数据集上进行的实验显示,拟议的方法大大改进了预测轨迹的准确性。