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。