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

“我在哪儿？”_这是当今许多自动驾驶汽车运行的主要出发点。通过了解自己的位置，他们可以继续评估还有什么，以及下一步要做什么。有效地做到这一点是具有挑战性的，因为自动驾驶汽车是非常复杂的实时系统，需要进行设计和工程。对于任何这样的系统，能够调试它们的开发过程是至关重要的，以便深入了解和理解为什么不同的系统配置执行不同。在工程术语中，获取车辆位置称为“定位”。在这一领域开发技术的一个关键要素是，能够定义一个“基本真实”的参考，根据这个参考，可以在不同的实验测试条件下评估自动驾驶汽车的性能。这可能是为了响应不断变化的环境条件（例如天气），不同的传感器配置（例如激光雷达加摄像头，以及它们正在寻找的位置），或者与外部因素（例如网络攻击）有关。这个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——可以用来测量车辆的定位，在它认为的位置和它在路上的实际位置之间进行比较。测试结果可以被开发团队用来快速有效地测量不同车辆传感器的性能，以及它们是如何配置的，以及了解软件是如何做出决策的。至关重要的是，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），并且可以在真实道路驾驶条件下工作（即在正常速度下，在不同的天气条件下等）。此外，它完全独立于被评估的传感器或软件组件，因此开发团队可以从自动驾驶汽车开发的早期到后期，用它来客观地验证和优化性能。这将成为一种非常有价值的工具，可以实现4级及以上的自动驾驶，并在开发和验证过程中独立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."