End-to-end motion planning models equipped with deep neural networks have
shown great potential for enabling full autonomous driving. However, the
oversized neural networks render them impractical for deployment on
resource-constrained systems, which unavoidably requires more computational
time and resources during reference.To handle this, knowledge distillation
offers a promising approach that compresses models by enabling a smaller
student model to learn from a larger teacher model. Nevertheless, how to apply
knowledge distillation to compress motion planners has not been explored so
far. In this paper, we propose PlanKD, the first knowledge distillation
framework tailored for compressing end-to-end motion planners. First,
considering that driving scenes are inherently complex, often containing
planning-irrelevant or even noisy information, transferring such information is
not beneficial for the student planner. Thus, we design an information
bottleneck based strategy to only distill planning-relevant information, rather
than transfer all information indiscriminately. Second, different waypoints in
an output planned trajectory may hold varying degrees of importance for motion
planning, where a slight deviation in certain crucial waypoints might lead to a
collision. Therefore, we devise a safety-aware waypoint-attentive distillation
module that assigns adaptive weights to different waypoints based on the
importance, to encourage the student to accurately mimic more crucial
waypoints, thereby improving overall safety. Experiments demonstrate that our
PlanKD can boost the performance of smaller planners by a large margin, and
significantly reduce their reference time.

本文提出 PlanKD，这是第一个专门用于压缩端到端运动规划器的知识蒸馏框架，通过信息瓶颈策略和安全感知的逐点蒸馏模块，可以显著提升较小规模规划器的性能并减少参考时间。

迈向端到端自动驾驶运动规划器压缩的便携性

On the Road to Portability: Compressing End-to-End Motion Planner for  Autonomous Driving

Hierarchical Agglomerative Clustering (HAC) is likely the earliest and most
flexible clustering method, because it can be used with many distances,
similarities, and various linkage strategies. It is often used when the number
of clusters the data set forms is unknown and some sort of hierarchy in the
data is plausible. Most algorithms for HAC operate on a full distance matrix,
and therefore require quadratic memory. The standard algorithm also has cubic
runtime to produce a full hierarchy. Both memory and runtime are especially
problematic in the context of embedded or otherwise very resource-constrained
systems. In this section, we present how data aggregation with BETULA, a
numerically stable version of the well known BIRCH data aggregation algorithm,
can be used to make HAC viable on systems with constrained resources with only
small losses on clustering quality, and hence allow exploratory data analysis
of very large data sets.

使用 BETULA 数据聚合算法进行数据聚类，使得在资源受限的系统上使用层次凝聚聚类方法成为可能，仅在聚类质量上有轻微损失，并且允许对非常大的数据集进行探索性数据分析。

层次聚类的数据聚合

Data Aggregation for Hierarchical Clustering

Consumption Markov Decision Processes (CMDPs) are probabilistic
decision-making models of resource-constrained systems. In a CMDP, the
controller possesses a certain amount of a critical resource, such as electric
power. Each action of the controller can consume some amount of the resource.
Resource replenishment is only possible in special reload states, in which the
resource level can be reloaded up to the full capacity of the system. The task
of the controller is to prevent resource exhaustion, i.e. ensure that the
available amount of the resource stays non-negative, while ensuring an
additional linear-time property. We study the complexity of strategy synthesis
in consumption MDPs with almost-sure B\"uchi objectives. We show that the
problem can be solved in polynomial time. We implement our algorithm and show
that it can efficiently solve CMDPs modelling real-world scenarios.

本文研究了 Consumption Markov Decision Processes（CMDPs）中具有几乎确定的 B"uchi 对象的策略综合的复杂性，并证明了该问题可以在多项式时间内解决。 我们实施了该算法，并证明它可以有效地解决模拟真实情景的 CMDP 问题。