RoadLoc: A development and test framework for ground-truth vehicle localisation.

_我在哪里？_这是当今许多自动驾驶车辆运营的主要起点。通过了解自己的位置，他们可以继续评估未来还会发生什么，以及下一步要做什么。要有效地做到这一点是具有挑战性的，因为自动驾驶汽车的设计和工程是极其复杂的实时系统。与任何此类系统一样，能够调试它们的开发过程，以便洞察和理解不同系统配置为何表现不同，这一点至关重要。在工程术语中，获得车辆位置称为本地化。在这一领域开发技术的一个关键要素是能够定义一个**地面真实**参考，根据该参考可以在不同的实验测试条件下评估自动驾驶汽车的性能。这可能是对不断变化的环境条件(例如天气)、不同的传感器配置(例如LiDAR和摄像头，以及它们正在查看的位置)的响应，或者与外部因素(例如网络攻击)有关。该项目提出了一个创新的框架-由硬件传感器和分析软件组成-可以用来测量车辆在**认为**它所在的位置与它**实际**在道路上的位置之间的定位。开发团队可以使用该结果快速有效地测量不同车辆传感器的性能以及它们如何配置为运行，以及了解软件是如何做出决策的。关键是，该框架不需要额外的外部基础设施来运行(与增强型GNSS解决方案不同)，并且可以在真实的道路驾驶条件下工作(即在正常速度下、在变化的天气条件下等)。此外，它完全独立于被评估的传感器或软件组件-因此可以用于开发团队从自动驾驶汽车开发的非常早期到后期的客观验证和优化性能。这将产生一个极其有价值的工具，用于实现4级及以上的自主性，并在开发和验证过程中独立地提供关键的安全评估。“

"_Where am I?_ This is the main starting point for the operation of many of today's self-driving vehicles. From knowing their position, they can go on to assess what else is out there, and what to do next. To do this effectively is challenging , as autonomous vehicles are incredibly complex real-time systems to design and engineer. As with any such system, it is crucial to be able to _debug_ their development process in order to gain insight and understanding of why varying system configurations perform differently.In engineering terms, obtaining the vehicle position is known as _localization_. A key element of developing technologies in this area, is to be able to define a **ground-truth** reference against which the performance of the autonomous vehicle can be assessed under different experimental test conditions. This might be in response to changing environmental conditions (e.g. weather), different sensors configurations (e.g. LiDAR plus cameras, and where they are looking) , or in relation to external factors (e.g. cyber-attack).This project sets out an innovative framework - consisting of a hardware sensor and analytics software - that can be used to measure the localisation of the vehicle between where it **thinks** it is, compared to where it **actually** is on the road. The result can be used by the development teams to quickly and effectively measure the performance of different vehicle sensor and how they are configured to operate, as well as understanding how the software is making decisions.Crucially this framework requires no additional external infrastructure to operate (unlike enhanced GNSS solutions) and can work under real road driving conditions (i.e. at normal speeds, under varying weather conditions, etc.). Furthermore, it is completely independent of the sensor or software component being evaluated - so can be used to objectively verify and optimise performance by the development team from the very early to late stages of self-driving vehicle development. This will result in an extremely valuable tool for enabling Level 4 autonomy and beyond, as well as independently delivering critical assessment of safety during the development and validation process."