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.

翻译：自动驾驶汽车的感知模块依赖多传感器系统来理解其环境。深度学习领域的最新进展推动了整合多传感测量以增强感知能力的方法的快速发展。本文综述了当前应用于自动驾驶系统感知模块的最新深度学习整合技术，并基于“整合什么、如何整合以及何时整合”对整合方法进行了分类。提出了一种新的整合分类体系，涵盖三个维度：多视角、多模态和多帧。本文总结了整合操作及其优缺点，为“理想”数据整合方法的特性提供了新见解，该方法能够减轻现有方法的局限性。在回顾数百篇相关论文后，本综述以对最优数据整合方法关键特征的讨论作为结论。