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

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.

Publications

• S. Huster, J. Götz, K. David, D. Reinhardt. Privacy Threats in Cooperative Collision Avoidance System Architectures. IEEE Transactions on Intelligent Vehicles, 2025. Accepted for publication.
  

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