论文标题
SCEPT：符合场景的，基于政策的轨迹预测计划
ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning
论文作者
论文摘要
轨迹预测是自主系统的关键功能，它们与不受控制的代理共享环境，一个重要的例子是自动驾驶车辆。当前，大多数预测方法都不强制执行场景一致性，即，在场景中不同代理的预测轨迹之间存在大量的自我colli缩。此外，许多方法都会产生每个代理的单个轨迹预测，而不是整个场景的联合轨迹预测，这使下游计划变得困难。在这项工作中，我们提出了Scept，这是一个基于政策计划的轨迹预测模型，该模型生成了适合自主系统运动计划的准确，场景符合的轨迹预测。它明确地执行了场景一致性，并学习了可用于有条件预测的代理交互策略。在多个现实世界中的行人和自动驾驶汽车数据集上进行的实验表明，Scept}将当前的最新预测准确性与场景一致性相匹配。我们还展示了Secpe与下游临时性计划者合作的能力。
Trajectory prediction is a critical functionality of autonomous systems that share environments with uncontrolled agents, one prominent example being self-driving vehicles. Currently, most prediction methods do not enforce scene consistency, i.e., there are a substantial amount of self-collisions between predicted trajectories of different agents in the scene. Moreover, many approaches generate individual trajectory predictions per agent instead of joint trajectory predictions of the whole scene, which makes downstream planning difficult. In this work, we present ScePT, a policy planning-based trajectory prediction model that generates accurate, scene-consistent trajectory predictions suitable for autonomous system motion planning. It explicitly enforces scene consistency and learns an agent interaction policy that can be used for conditional prediction. Experiments on multiple real-world pedestrians and autonomous vehicle datasets show that ScePT} matches current state-of-the-art prediction accuracy with significantly improved scene consistency. We also demonstrate ScePT's ability to work with a downstream contingency planner.
