The perception module of self-driving vehicles relies on a multi-sensor
system to understand its environment. Recent advancements in deep learning have
led to the rapid development of approaches that integrate multi-sensory
measurements to enhance perception capabilities. This paper surveys the latest
deep learning integration techniques applied to the perception module in
autonomous driving systems, categorizing integration approaches based on "what,
how, and when to integrate." A new taxonomy of integration is proposed, based
on three dimensions: multi-view, multi-modality, and multi-frame. The
integration operations and their pros and cons are summarized, providing new
insights into the properties of an "ideal" data integration approach that can
alleviate the limitations of existing methods. After reviewing hundreds of
relevant papers, this survey concludes with a discussion of the key features of
an optimal data integration approach.

本文综述了近期深度学习与感知模块相结合的自动驾驶系统，介绍了针对何时、如何和何种方式集成多传感器测量的分类方法，提出了基于多视图、多模态和多帧的三维集成分类，总结了集成操作的优缺点，并讨论了理想数据集成方法的关键特性。

自动驾驶系统中数据集成的深度学习方法调查

A survey on deep learning approaches for data integration in autonomous  driving system

Remote state estimation of large-scale distributed dynamic processes plays an
important role in Industry 4.0 applications. In this paper, by leveraging the
theoretical results of structural properties of optimal scheduling policies, we
develop a structure-enhanced deep reinforcement learning (DRL) framework for
optimal scheduling of a multi-sensor remote estimation system to achieve the
minimum overall estimation mean-square error (MSE). In particular, we propose a
structure-enhanced action selection method, which tends to select actions that
obey the policy structure. This explores the action space more effectively and
enhances the learning efficiency of DRL agents. Furthermore, we introduce a
structure-enhanced loss function to add penalty to actions that do not follow
the policy structure. The new loss function guides the DRL to converge to the
optimal policy structure quickly. Our numerical results show that the proposed
structure-enhanced DRL algorithms can save the training time by 50% and reduce
the remote estimation MSE by 10% to 25%, when compared to benchmark DRL
algorithms.

本文研发了一种结构增强的深度强化学习算法用于最小化远程估计均方误差，实现在多传感器远程估计系统的最优调度，该算法可以比基准算法节省 50％的训练时间并将远程估计均方误差降低 10％至 25％。