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Scene Induced Multi-Modal Trajectory Forecasting via Planning

2019-05-23 22:00:17
Nachiket Deo, Mohan M. Trivedi

Abstract

We address multi-modal trajectory forecasting of agents in unknown scenes by formulating it as a planning problem. We present an approach consisting of three models; a goal prediction model to identify potential goals of the agent, an inverse reinforcement learning model to plan optimal paths to each goal, and a trajectory generator to obtain future trajectories along the planned paths. Analysis of predictions on the Stanford drone dataset, shows generalizability of our approach to novel scenes.

Abstract (translated)

将未知场景下的智能体多模态轨迹预测问题定义为规划问题。我们提出了一种由三个模型组成的方法:一个目标预测模型来识别代理的潜在目标,一个反向强化学习模型来规划每个目标的最佳路径,以及一个轨迹生成器来获得沿计划路径的未来轨迹。对斯坦福无人机数据集的预测分析表明,我们对新场景的方法具有普遍性。

URL

https://arxiv.org/abs/1905.09949

PDF

https://arxiv.org/pdf/1905.09949.pdf


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