端到端自动驾驶系统研究综述  

Survey of end-to-end autonomous driving systems

作　　者：陈妍妍 田大新[1] 林椿眄 殷鸿博 Chen Yanyan;Tian Daxin;Lin Chunmian;Yin Hongbo(School of Transportation Science and Engineering,Beihang University,Beijing 102200,China)

机构地区：[1]北京航空航天大学交通科学与工程学院,北京102200

出　　处：《中国图象图形学报》2024年第11期3216-3237,共22页Journal of Image and Graphics

基　　金：国家自然科学基金项目(U20A20155,62173012,52202391)。

摘　　要：近年深度学习技术助力端到端自动驾驶框架的发展和进步,涌现出一系列创新研究议题与应用部署方案。本文首先以经典的模块化系统切入,对自动驾驶感知—预测—规划—决策4大功能模块进行简要概述,分析传统的模块化和多任务方法的局限性;其次从输入—输出模态到系统架构角度对当前新兴的端到端自动驾驶框架进行广泛地调研,详细描述弱解释性端到端与模块化联合端到端两大主流范式,深入探究现有研究工作存在的不足和弊端;之后简单介绍了端到端自动驾驶系统的开环—闭环评估方法及适用场景;最后总结了端到端自动驾驶系统的研究工作,并从数据挖掘和架构设计角度展望领域潜在挑战和亟待解决的关键问题。Deep learning technologies have accelerated the development and advancement of end-to-end autonomous driving frameworks in recent years,sparking the emergence of numerous cutting-edge research topics and application deployment solutions.The“divide and conquer”architecture design concept,which aims to construct multiple independent but related module components,integrate them into the developed software system in a specific semantic or geometric order,and ultimately deploy these components to the actual vehicle,is the foundation for the majority of the autonomous driving systems currently in use,also known as modular systems.However,a well-developed modular design typically comprises thousands of components,placing a considerable burden on the graphics memory and processing capacity of automotive CPUs.Furthermore,the intrinsic mistakes of each stacked module during prediction will rise with the number of stacked modules,and upstream flaws cannot be fixed in downstream modules,presenting a major risk to vehicle safety.A multitask architecture based on the“task parallelism”principle aims to efficiently infer multiple tasks in parallel by designing various decoded heads with a shared backbone network to reduce computational consumption.However,the optimization goals for various tasks may not be consistent,and sharing features mindlessly can even degrade the overall performance of the system.In contrast to the previous two system architectures,the end-to-end technology paradigm eliminates information bottlenecks and cumulative errors due to the integration of numerous intermediate components based on rule interfaces,allowing the network to continually optimize toward a unified objective.A large model can be used to generate low-level control signals or vehicle motion planning based on inputs such as sensor data and vehicle status.With sensors serving as inputs,the early end-to-end design based on imitation and reinforcement learning directly outputs the final control commands for steering,braking,and accelerat

关 键 词：人工智能(AI) 自动驾驶 模块式系统 端到端系统 数据驱动 可解释性 

分 类 号：U495[交通运输工程—交通运输规划与管理]