ACHIEVE: A Common Evaluation Method for Simultaneous Privacy Protection and Collision Detection Performance of CooperativeCollision Avoidance Architectures for Vulnerable Road Users

2019年，德国道路交通事故造成862名弱势道路使用者（包括行人和骑自行车的人）死亡，11.6万人受伤。为了在未来减少这一数字，部署协同防撞系统似乎是一个很有前途的解决方案，在这种系统中，VRU和汽车驾驶员都配备了一个设备，可以警告他们即将发生的碰撞。为了实现这种协同保护系统，可以采用多种不同的架构。例如，它们的范围从具有设备到设备（D2D）通信和本地处理的点对点（P2P）体系结构，到基于混合通信（蜂窝和D2D）的体系结构，其处理分布在汽车、VRU和服务器上。这些不同的架构不仅影响了碰撞检测的性能，而且对用户隐私造成了不同的威胁。这源于在体系结构中收集、处理和交换关于这些用户的信息。从法律角度来看，保护用户隐私不仅是强制性的，而且我们希望这也有助于未来用户接受这项技术。虽然不同的架构设计已经被单独提出和评估，但是，目前的技术水平不允许确定哪种架构既提供最佳的碰撞检测性能又提供最佳的隐私保护，也就是说，还没有方法可以比较不同的架构在碰撞性能和/或隐私保护方面的优劣。这导致了我们项目的主要研究问题：我们如何在碰撞检测和隐私保护方面评估和比较协作避碰系统的可能架构的性能？因此，在ACHIEVE中，我们将开发一种基于这两个关键方面的新方法来评估和比较不同的架构。为了实现这一目标，我们将首先通过识别多个体系结构变体并对这些体系结构进行彻底的隐私威胁分析来构建此提议的基础。我们将进一步分析引入隐私保护解决方案对碰撞检测性能的影响。接下来，我们将重点关注关键性能指标的定义和分析，这些指标将反映碰撞检测性能和隐私保护，并将集成到我们的通用方法中。此外，为了提供这种新方法，我们将把它应用于感兴趣的体系结构，并对它们的性能进行全面的评估。通过利用我们的方法和评估结果，可以比较可能的体系结构变体，从而根据它们在碰撞检测和隐私保护方面的性能做出明智的决策。

In 2019, 862 Vulnerable Road Users (VRUs), including pedestrians and bicyclists, died and 116,000 were injured due to road traffic accidents in Germany. To reduce these numbers in the future, the deployment of cooperative collision avoidance systems, in which both VRU and car driver are equipped with a device that can warn them about upcoming collisions, appears to be a promising solution. For the realization of such a cooperative protection system, many different architectures are possible. For example, they range from Peer-to-Peer (P2P) architectures with Device-to-Device (D2D) communication and local processing to architectures based on hybrid communication (cellular and D2D), with processing distributed over the car, VRU, and server. These different architectures not only influence the collision detection performance, but also cause different threats to users’ privacy. This results from the collection, processing, and exchange of information about these users across the architecture. Protecting users’ privacy is not only mandatory from a legal perspective, but we expect that it will also contribute to the acceptance of this technology by future users. While different architecture designs have been proposed and evaluated in isolation, the state of the art does, however, not allow to determine which architecture(s) offer(s) both the best collision detection performance and privacy protection, i.e. no method to compare the different architectures with respect to collision performance and/or privacy protection is available yet. This leads to the main research question of our project: How can we evaluate and compare the performance of the possible architectures for a cooperative collision avoidance system in terms of both collision detection and privacy protection? In ACHIEVE, we will therefore develop a new method to assess and compare different architectures based on these two key aspects. To reach this goal, we will first build the basis of this proposal by identifying the multiple architecture variants and conducting a thorough privacy threat analysis of these architectures. We will further analyze the impact of the introduction of privacy-preserving solutions on the collision detection performance. We will next focus on the definition and analysis of key performance indicators that will reflect both the collision detection performances and privacy protection and will be integrated in our common method. In addition, to provide this new method, we will apply it to the architectures of interest and conduct a comprehensive evaluation of their performance. By leveraging our method and the results of our evaluation, it will be possible to compare the possible architecture variants and hence take informed decisions based on their performance in terms of both collision detection and privacy protection.