论文标题
M2i：从分支的边际轨迹预测到交互式预测
M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction
论文作者
论文摘要
预测公路参与者的未来动议是在城市场景中自动驾驶的重要任务。现有模型在预测单个代理的边际轨迹方面表现出色，但是在多个代理上共同预测场景符合场景的轨迹仍然是一个悬而未决的问题。挑战是由于指数增加了预测空间，这是代理数量的函数。在这项工作中，我们利用了相互作用的代理之间的潜在关系，并将关节预测问题分解为边际预测问题。我们提出的方法M2i首先将相互作用的剂分类为有影响力的人和反应堆，然后利用边缘预测模型和条件预测模型分别预测影响者和反应器的轨迹。相互作用剂的预测是根据其联合可能性组合并选择的。实验表明，我们简单但有效的方法在Waymo Open Motion数据集交互式预测基准上实现了最先进的性能。
Predicting future motions of road participants is an important task for driving autonomously in urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet it remains an open question to jointly predict scene compliant trajectories over multiple agents. The challenge is due to exponentially increasing prediction space as a function of the number of agents. In this work, we exploit the underlying relations between interacting agents and decouple the joint prediction problem into marginal prediction problems. Our proposed approach M2I first classifies interacting agents as pairs of influencers and reactors, and then leverages a marginal prediction model and a conditional prediction model to predict trajectories for the influencers and reactors, respectively. The predictions from interacting agents are combined and selected according to their joint likelihoods. Experiments show that our simple but effective approach achieves state-of-the-art performance on the Waymo Open Motion Dataset interactive prediction benchmark.
