In autonomous navigation, a planning system reasons about other agents to
plan a safe and plausible trajectory. Before planning starts, agents are
typically processed with computationally intensive models for recognition,
tracking, motion estimation and prediction. With limited computational
resources and a large number of agents to process in real time, it becomes
important to efficiently rank agents according to their impact on the decision
making process. This allows spending more time processing the most important
agents. We propose a system to rank agents around an autonomous vehicle (AV) in
real time. We automatically generate a ranking data set by running the planner
in simulation on real-world logged data, where we can afford to run more
accurate and expensive models on all the agents. The causes of various planner
actions are logged and used for assigning ground truth importance scores. The
generated data set can be used to learn ranking models. In particular, we show
the utility of combining learned features, via a convolutional neural network,
with engineered features designed to capture domain knowledge. We show the
benefits of various design choices experimentally. When tested on real AVs, our
system demonstrates the capability of understanding complex driving situations.

提出一种实时排名系统，通过仿真运行规划器并记录各种规划器活动的结果自动生成一个代理编排数据集，并结合卷积神经网络和领域知识工程特征，用于实现在自主导航中智能代理编排。

自主导航的代理优先级

Agent Prioritization for Autonomous Navigation

LAMA is a classical planning system based on heuristic forward search. Its
core feature is the use of a pseudo-heuristic derived from landmarks,
propositional formulas that must be true in every solution of a planning task.
LAMA builds on the Fast Downward planning system, using finite-domain rather
than binary state variables and multi-heuristic search. The latter is employed
to combine the landmark heuristic with a variant of the well-known FF
heuristic. Both heuristics are cost-sensitive, focusing on high-quality
solutions in the case where actions have non-uniform cost. A weighted A* search
is used with iteratively decreasing weights, so that the planner continues to
search for plans of better quality until the search is terminated. LAMA showed
best performance among all planners in the sequential satisficing track of the
International Planning Competition 2008. In this paper we present the system in
detail and investigate which features of LAMA are crucial for its performance.
We present individual results for some of the domains used at the competition,
demonstrating good and bad cases for the techniques implemented in LAMA.
Overall, we find that using landmarks improves performance, whereas the
incorporation of action costs into the heuristic estimators proves not to be
beneficial. We show that in some domains a search that ignores cost solves far
more problems, raising the question of how to deal with action costs more
effectively in the future. The iterated weighted A* search greatly improves
results, and shows synergy effects with the use of landmarks.

LAMA 是一个基于启发式正向搜索的经典规划系统，利用里程碑派生的伪启发式是其核心特征，结合具备成本敏感性的两种启发式可以面对具有非均匀成本的动作，迭代加权 A * 搜索可持续提高搜索质量并表现出与使用里程碑有协同效应；LAMA 在国际计划竞赛 2008 的顺序满足赛道表现最佳，但它的启发式评估中融入动作成本并不具优势，导致在某些情况下，不考虑成本的搜索方法更加成功，这引出了今后应如何更有效地处理行动成本的问题。

LAMA Planner: 以标志物为导向的成本感知规划算法

The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks

The SHOP2 planning system received one of the awards for distinguished
performance in the 2002 International Planning Competition. This paper
describes the features of SHOP2 which enabled it to excel in the competition,
especially those aspects of SHOP2 that deal with temporal and metric planning
domains.

SHOP2 规划系统在 2002 年国际规划竞赛中获得杰出表现奖。本文介绍了 SHOP2 的特点，特别是处理时间和指标规划领域的方面。