论文标题
使用双层隔离模型与闭塞交通剂进行计划
Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models
论文作者
论文摘要
用遮挡的交通代理进行推理是计划自动驾驶汽车的重大开放挑战。最近的深度学习模型显示了基于附近可见剂的行为预测遮挡剂的令人印象深刻的结果。但是，正如我们在实验中所显示的那样，这些模型很难集成到下游计划中。为此，我们提出了双层变化遮挡模型（BIVO），这是一种两步生成模型，首先预测闭塞剂的可能位置，然后为遮挡剂生成可能的轨迹。与现有方法相反，BIVO输出了一个轨迹分布，然后可以将其从并集成到标准下游计划中。我们使用现实世界的Nuscenes数据集评估了闭环重放模拟中的方法。我们的结果表明，Bivo可以成功学习预测封闭的剂轨迹，这些预测会在关键场景中更好地随后的运动计划。
Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles. Recent deep learning models have shown impressive results for predicting occluded agents based on the behaviour of nearby visible agents; however, as we show in experiments, these models are difficult to integrate into downstream planning. To this end, we propose Bi-level Variational Occlusion Models (BiVO), a two-step generative model that first predicts likely locations of occluded agents, and then generates likely trajectories for the occluded agents. In contrast to existing methods, BiVO outputs a trajectory distribution which can then be sampled from and integrated into standard downstream planning. We evaluate the method in closed-loop replay simulation using the real-world nuScenes dataset. Our results suggest that BiVO can successfully learn to predict occluded agent trajectories, and these predictions lead to better subsequent motion plans in critical scenarios.
