论文标题
以基于网格的计划为条件的未知环境中的轨迹预测
Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans
论文作者
论文摘要
我们解决了在未知环境中预测行人和车辆轨迹的问题，并以其过去的运动和场景结构为条件。轨迹预测是一个具有挑战性的问题，因为场景结构的差异很大和未来轨迹的多模式分布。与以前的方法不同的方法从观察到的上下文到将来的多个轨迹，我们建议根据从基于网格的策略中采样的计划来调节轨迹预测，该计划使用最大的熵逆强化学习（Maxent IRL）来学习。我们重新制定了Maxent IRL，以允许该政策共同推断出合理的代理目标，并在现场定义的粗略2-D网格上实现这些目标。我们提出了一个基于注意力的轨迹生成器，该发生器会生成以从Maxent策略采样的状态序列为条件的连续有价值的未来轨迹。对公共可用的斯坦福无人机和Nuscenes数据集的定量和定性评估表明，我们的模型会产生多种多样的轨迹，代表多模式预测分布，并精确地符合长期预测范围的基础场景结构。
We address the problem of forecasting pedestrian and vehicle trajectories in unknown environments, conditioned on their past motion and scene structure. Trajectory forecasting is a challenging problem due to the large variation in scene structure and the multimodal distribution of future trajectories. Unlike prior approaches that directly learn one-to-many mappings from observed context to multiple future trajectories, we propose to condition trajectory forecasts on plans sampled from a grid based policy learned using maximum entropy inverse reinforcement learning (MaxEnt IRL). We reformulate MaxEnt IRL to allow the policy to jointly infer plausible agent goals, and paths to those goals on a coarse 2-D grid defined over the scene. We propose an attention based trajectory generator that generates continuous valued future trajectories conditioned on state sequences sampled from the MaxEnt policy. Quantitative and qualitative evaluation on the publicly available Stanford drone and NuScenes datasets shows that our model generates trajectories that are diverse, representing the multimodal predictive distribution, and precise, conforming to the underlying scene structure over long prediction horizons.
