Security and privacy of current Car-to-X systems heavily depends on the usage of pseudonym certificates. These carry the required information for authenticating messages received from other vehicles. However, only a limited amount of detailed studies about certificate distribution strategies in VANETs as well as attack surfaces of such systems has been proposed. Therefore, a general study about possible distribution mechanisms and their parametrization is provided in this work. Thereby, the management of entries in request lists is identified as a key issue for system performance. Additionally, a design flaw in the currently standardized ETSI ITS distribution scheme is outlined leading to the possibility of an attacker significantly increasing channel load on the safety critical control channel. A solution to this problem is suggested and an evaluation of its performance is provided. Furthermore, the evaluation shows the great influence of request list management on authentication delay and thus on security inducted packet loss.

Automated vehicles need to interact: to create mutual awareness and to coordinate maneuvers. How this interaction shall be achieved is still an open issue. Several new protocols are discussed for cooperative services such as changing lanes or overtaking, e.g., within the European Telecommunications Standards Institute (ETSI) and Society of Automotive Engineers (SAE). These communication protocols are, however, usually specific to individual maneuvers or based on implicit assumptions on other vehicles' intentions. To enable reuse and support extensibility towards future maneuvers, we propose CVIP, a protocol framework for complex vehicular interactions. CVIP supports explicitly negotiating maneuvers between the involved vehicles and allows monitoring maneuver progress via status updates. We present our design in detail and demonstrate via simulations that it enables complex intervehicle interactions in a flexible, efficient and robust manner.We also discuss open questions to be answered before complex interactions among automated vehicles can become a reality.

No abstract

No abstract

A serious problem in image classification is that a trained model might perform well for input data that originates from the same distribution as the data available for model training, but performs much worse for out-of-distribution (OOD) samples. In real-world safety-critical applications, in particular, it is important to be aware if a new data point is OOD. To date, OOD detection is typically addressed using either confidence scores, autoencoder based reconstruction, or by contrastive learning. However, global image context has not yet been explored to discriminate the non-local objectness between in-distribution and OOD samples. This paper proposes a first-of-its-kind OOD detection architecture named OODformer that leverage the contextualization capabilities of the transformer. Incorporating the transformer as the principle feature extractor allows us to exploit the object concepts and their discriminate attributes along with their co-occurrence via visual attention. Using the contextualised embedding, we demonstrate OOD detection using both class-conditioned latent space similarity and a network confidence score. Our approach shows improved generalizability across various datasets. We have achieved a new state-of-the-art result on CIFAR-10/-100 and ImageNet30.