端到端学习与模块化架构：自动驾驶系统的比较洞见
End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems
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- Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）
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中文总结 AI 辅助
本文比较端到端学习与模块化架构在自动驾驶中的优劣，提出四维架构选择框架，并验证其能正确推荐10个系统中的7个，指出混合架构为未来方向。
中文摘要 AI 辅助
自动驾驶系统已成为智能交通研究的核心焦点，端到端学习与模块化架构为其实施提供了两种突出的设计范式。端到端学习利用深度学习算法将原始感官输入直接映射到驾驶执行器，提供了一种简化和适应性强的解决方案。而模块化架构采用基于流水线的方法，将系统划分为感知、认知、规划和控制等不同子系统。本文对这些范式进行了全面的比较分析，重点关注其优势、局限性和权衡，以提供对其在各种自动驾驶应用中适用性的洞见。研究评估了解释性、可扩展性、鲁棒性和实际应用性等关键因素。虽然端到端学习强调在动态环境中的简单性和适应性，但它缺乏透明度且高度依赖大型数据集。相反，模块化架构提供了优越的解释性和任务特定优化，但面临集成复杂性和可扩展性相关的挑战。为解决这些局限，结合两种范式优势的混合方法应运而生，为克服这些挑战提供了有前景的方向。在此比较综合之外，后续工作提出了一个四维架构选择框架，包含跨越安全性、运行环境、数据/计算资源和部署环境的十二个二元标准，并针对十个已发表的自动驾驶系统进行了验证，正确推荐了10个部署架构中的7个。这项工作综合了现有文献，以突出范式之间的关键权衡，并确定混合架构作为未来研究的有前景方向。
英文摘要
Autonomous driving systems have become a central focus of intelligent transportation research, with End-to-End Learning and Modular Architectures offering two prominent design paradigms for their implementation. E2E Learning uses deep learning algorithms to map raw sensory inputs directly to driving actuators, providing a streamlined and adaptable solution. while Modular Architectures employ a pipeline-based approach, dividing the system into distinct subsystems for perception, cognition, planning, and control. This paper presents a comprehensive comparative analysis of these paradigms, focusing on their strengths, limitations, and trade-offs to provide insights into their suitability for various autonomous driving applications. The study evaluates key factors such as interpretability, scalability, robustness, and real-world applicability. While End-to-End Learning emphasizes simplicity and adaptability in dynamic environments, it lacks transparency and is highly dependent on large datasets. Conversely, Modular Architectures offer superior interpretability and task-specific optimization, but face challenges related to integration complexity and scalability. To address these limitations, hybrid approaches that combine the strengths of both paradigms have emerged, offering a promising direction for overcoming these challenges. Beyond this comparative synthesis, following work proposes a Four-Dimensional Architecture Selection Framework, comprising twelve binary criteria across safety, operating environment, data/computational resources, and deployment context, and validate it against ten published autonomous driving systems, correctly recommending 7/10 deployed architectures. This work synthesizes existing literature to highlight key trade-offs between the paradigms and identifies hybrid architectures as a promising direction for future research.
