Trajectory forecasting is critical for autonomous platforms to make safe planning and actions. Currently, most trajectory forecasting methods assume that object trajectories have been extracted and directly develop trajectory predictors based on the ground truth trajectories. However, this assumption does not hold in practical situations. Trajectories obtained from object detection and tracking are inevitably noisy, which could cause serious forecasting errors to predictors built on ground truth trajectories. In this paper, we propose a trajectory predictor directly based on detection results without relying on explicitly formed trajectories. Different from the traditional methods which encode the motion cue of an agent based on its clearly defined trajectory, we extract the motion information only based on the affinity cues among detection results, in which an affinity-aware state update mechanism is designed to take the uncertainty of association into account. In addition, considering that there could be multiple plausible matching candidates, we aggregate the states of them. This design relaxes the undesirable effect of noisy trajectory obtained from data association. Extensive ablation experiments validate the effectiveness of our method and its generalization ability on different detectors. Cross-comparison to other forecasting schemes further proves the superiority of our method. Code will be released upon acceptance.
翻译:目前,大多数轨迹预测方法都假定物体轨迹已经提取,并直接根据地面真相轨迹开发轨道预测器。然而,这一假设在实际情况下并不有效。从物体探测和跟踪中获得的轨迹不可避免地噪音,这可能会给地面真相轨迹上的预测器造成严重的预测错误。在本文中,我们提议一个轨道预测器,直接以探测结果为基础,而不必依赖明确形成的轨迹。不同于根据明确定义的轨迹对一个代理人运动提示进行编码的传统方法,我们只根据探测结果之间的亲近提示提取运动信息,在探测结果中,一种亲近觉状态更新机制的设计是为了考虑到关联的不确定性。此外,考虑到可能有许多可信的匹配对象,我们将它们的状态汇总在一起。这一设计可以减轻从数据关联中获取的噪音轨迹的不良效应。广泛进行对比试验,验证我们方法的有效性及其在不同探测器上的普及能力。交叉对比法将进一步验证我们方法的优越性。