论文标题
使用轨迹集和自动驾驶领域知识的运动预测
Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge
论文作者
论文摘要
已经研究了使用各种技术，包括随机策略，生成模型和回归，可以预测车辆的未来运动。最近的工作表明，对轨迹集的分类，该轨迹集近似可能的动作，实现了最新的性能，并避免了模式崩溃之类的问题。但是，在此公式中，并未完全利用地图信息和附近轨迹之间的物理关系。我们通过添加辅助损失来惩罚越野预测，以基于分类的运动预测方法进行基于分类的运动预测方法。仅使用MAP信息（例如，越野区域）可以轻松地预测这种辅助损失，从而显着改善了小数据集的性能。我们还研究了加权的横向渗透损失，以捕获轨迹之间的时空关系。我们的最终贡献是对两个公共自动驾驶数据集的分类和序数回归的详细比较。
Predicting the future motion of vehicles has been studied using various techniques, including stochastic policies, generative models, and regression. Recent work has shown that classification over a trajectory set, which approximates possible motions, achieves state-of-the-art performance and avoids issues like mode collapse. However, map information and the physical relationships between nearby trajectories is not fully exploited in this formulation. We build on classification-based approaches to motion prediction by adding an auxiliary loss that penalizes off-road predictions. This auxiliary loss can easily be pretrained using only map information (e.g., off-road area), which significantly improves performance on small datasets. We also investigate weighted cross-entropy losses to capture spatial-temporal relationships among trajectories. Our final contribution is a detailed comparison of classification and ordinal regression on two public self-driving datasets.
