论文标题
随着周期一致性损失，改善自动驾驶的运动预测
Improving Motion Forecasting for Autonomous Driving with the Cycle Consistency Loss
论文作者
论文摘要
动态场景的强大运动预测是自动驾驶汽车的关键组成部分。由于场景中的异质性以及问题的固有不确定性，这是一个具有挑战性的问题。为了提高运动预测的准确性，在这项工作中，我们确定了该任务中的新一致性约束，这是代理的未来轨迹应该与其历史观察结果相一致，反之亦然。为了利用这一特性，我们提出了一种新颖的周期一致性训练方案，并定义了新的周期损失，以鼓励这种一致性。特别是，我们将预测的未来轨迹倒流倒流，并将其倒入预测模型中，以预测历史记录并将损失视为额外的周期损失项。通过我们在Argoverse数据集上的实验，我们证明了循环损失可以改善竞争运动预测模型的性能。
Robust motion forecasting of the dynamic scene is a critical component of an autonomous vehicle. It is a challenging problem due to the heterogeneity in the scene and the inherent uncertainties in the problem. To improve the accuracy of motion forecasting, in this work, we identify a new consistency constraint in this task, that is an agent's future trajectory should be coherent with its history observations and visa versa. To leverage this property, we propose a novel cycle consistency training scheme and define a novel cycle loss to encourage this consistency. In particular, we reverse the predicted future trajectory backward in time and feed it back into the prediction model to predict the history and compute the loss as an additional cycle loss term. Through our experiments on the Argoverse dataset, we demonstrate that cycle loss can improve the performance of competitive motion forecasting models.
