论文标题
TNT：目标驱动轨迹预测
TNT: Target-driveN Trajectory Prediction
论文作者
论文摘要
预测移动代理的未来行为对于现实世界的应用至关重要。这是具有挑战性的，因为代理的意图和相应的行为是未知的，并且本质上是多模式的。我们的关键见解是，要在适度的时间范围内进行预测，可以有效地捕获一组目标状态的未来模式。这导致我们的目标驱动轨迹预测（TNT）框架。 TNT有三个阶段，这些阶段是端到端训练的。它首先通过编码其与环境和其他代理商的互动来预测代理商的潜在目标$ t $步骤。然后，TNT生成以目标为条件的轨迹状态序列。最终阶段估计轨迹可能性和最终的紧凑型轨迹预测集。这与以前的工作形成鲜明对比的是，该工作将代理将其视为潜在变量并依赖于测试时间抽样来生成各种轨迹。我们基于对车辆和行人的轨迹预测进行基准测试，在该预测上，我们在Argoverse预测，互动，Stanford Drone和内部交流数据集方面表现出色。
Predicting the future behavior of moving agents is essential for real world applications. It is challenging as the intent of the agent and the corresponding behavior is unknown and intrinsically multimodal. Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captured by a set of target states. This leads to our target-driven trajectory prediction (TNT) framework. TNT has three stages which are trained end-to-end. It first predicts an agent's potential target states $T$ steps into the future, by encoding its interactions with the environment and the other agents. TNT then generates trajectory state sequences conditioned on targets. A final stage estimates trajectory likelihoods and a final compact set of trajectory predictions is selected. This is in contrast to previous work which models agent intents as latent variables, and relies on test-time sampling to generate diverse trajectories. We benchmark TNT on trajectory prediction of vehicles and pedestrians, where we outperform state-of-the-art on Argoverse Forecasting, INTERACTION, Stanford Drone and an in-house Pedestrian-at-Intersection dataset.
