An intelligent agent operating in the real-world must balance achieving its
goal with maintaining the safety and comfort of not only itself, but also other
participants within the surrounding scene. This requires jointly reasoning
about the behavior of other actors while deciding its own actions as these two
processes are inherently intertwined - a vehicle will yield to us if we decide
to proceed first at the intersection but will proceed first if we decide to
yield. However, this is not captured in most self-driving pipelines, where
planning follows prediction. In this paper we propose a novel data-driven,
reactive planning objective which allows a self-driving vehicle to jointly
reason about its own plans as well as how other actors will react to them. We
formulate the problem as an energy-based deep structured model that is learned
from observational data and encodes both the planning and prediction problems.
Through simulations based on both real-world driving and synthetically
generated dense traffic, we demonstrate that our reactive model outperforms a
non-reactive variant in successfully completing highly complex maneuvers (lane
merges/turns in traffic) faster, without trading off collision rate.

通过深度结构模型的数据驱动反应式规划目标，允许自动驾驶车辆在制定自己的计划的同时，联合推理其他参与者的行为，能够更快地完成高度复杂的机动（道路合并 / 拐弯），而不会牺牲碰撞率，比非反应式情况更具优势

深度结构反应规划

Deep Structured Reactive Planning

Deep ConvNets have been shown to be effective for the task of human pose
estimation from single images. However, several challenging issues arise in the
video-based case such as self-occlusion, motion blur, and uncommon poses with
few or no examples in training data sets. Temporal information can provide
additional cues about the location of body joints and help to alleviate these
issues. In this paper, we propose a deep structured model to estimate a
sequence of human poses in unconstrained videos. This model can be efficiently
trained in an end-to-end manner and is capable of representing appearance of
body joints and their spatio-temporal relationships simultaneously. Domain
knowledge about the human body is explicitly incorporated into the network
providing effective priors to regularize the skeletal structure and to enforce
temporal consistency. The proposed end-to-end architecture is evaluated on two
widely used benchmarks (Penn Action dataset and JHMDB dataset) for video-based
pose estimation. Our approach significantly outperforms the existing
state-of-the-art methods.

本文提出了一种深度结构模型，用于在不受限制的视频中预测人体姿态序列，借助于深度卷积神经网络、时间信息和领域知识，该模型能够同时表示身体关节的外观和它们的时空关系，并有效约束骨骼结构和强制时间一致性。在两个广泛使用的基准测试数据集上评估该架构，取得了比现有最先进方法更好的性能表现。