解决自动驾驶软件栈集成难题，提升科研算法落地实车效率。
Approaching Current Challenges in Developing a Software Stack for Fully Autonomous Driving
- 从TUM自动驾驶赛车项目中提炼出科研算法部署到实车的通用挑战。
- 提出可复用的开发策略与开源实现，降低全栈系统集成难度。
- 适合关注自动驾驶系统集成、科研成果工程化的研究者和开发者。
自动驾驶是一项复杂任务，通常通过模块化将驾驶任务分解为多个子任务，各子模块独立开发与发布。然而，当这些独立开发的算法需重新整合为完整的自动驾驶软件栈时，会面临特殊挑战。基于我们在TUM自动驾驶赛车项目中的实践经验，本文系统识别并总结了科研环境中构建自动驾驶软件栈所面临的通用难题，不聚焦于具体算法的技术细节，而是关注研究成果在真实测试车辆上部署时的关键障碍。为此，我们提出了有效的应对策略，并提供GitHub上的开源实现。该工作旨在简化未来全栈自动驾驶项目的开发流程，为各算法的全面评估提供坚实基础。
原文摘要 · Abstract (English)
Autonomous driving is a complex undertaking. A common approach is to break down the driving task into individual subtasks through modularization. These sub-modules are usually developed and published separately. However, if these individually developed algorithms have to be combined again to form a full-stack autonomous driving software, this poses particular challenges. Drawing upon our practical experience in developing the software of TUM Autonomous Motorsport, we have identified and derived these challenges in developing an autonomous driving software stack within a scientific environment. We do not focus on the specific challenges of individual algorithms but on the general difficulties that arise when deploying research algorithms on real-world test vehicles. To overcome these challenges, we introduce strategies that have been effective in our development approach. We additionally provide open-source implementations that enable these concepts on GitHub. As a result, this paper's contributions will simplify future full-stack autonomous driving projects, which are essential for a thorough evaluation of the individual algorithms.
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