Message Authentication Codes (MACs) represent a fundamental symmetric key primitive, serving to ensure the authenticity and integrity of transmitted data. As a building block in authenticated encryption and in numerous deployed standards, including TLS, IPsec, and SSH, MACs play a central role in practice. Due to their importance for practice, MACs have been subject to extensive research, leading to prominent schemes such as HMAC, CBCMAC, or LightMAC. Despite the existence of various MACs, there is still considerable interest in creating schemes that are more efficient, potentially parallelizable, or have specific non-cryptographic attributes, such as being patent-free. In this context, we introduce an automated method for analyzing and synthesizing MAC schemes. In order to achieve this goal, we have constructed a framework that restricts the class of MACs in such a way that it is sufficiently expressive to cover known constructions, yet also admits automated reasoning about the security guarantees of both known and new schemes. Our automated analysis has identified a novel category of MACs, termed “hybrid” MACs. These MACs operate by processing multiple blocks concurrently, with each block managed by a different, specified MAC scheme. A key finding is that in certain scenarios, the hybrid MAC marginally outperforms the simultaneous operation of the individual MACs. This improvement is attributed to the hybrid approach exploiting the strengths and compensating for the weaknesses of each distinct MAC scheme involved. Our implementation confirms that we have successfully identified new schemes that have comparable performance with state-of-the-art schemes and in some settings seem to be slightly more efficient.

For autonomous driving, knowledge about the current environment and especially the driveable lanes is of utmost importance. Currently this information is often extracted from meticulously (hand-)crafted offline high-definition maps, restricting the operation of autonomous vehicles to few well-mapped areas and making it vulnerable to temporary or permanent environment changes. This paper addresses the issues of map-based road models by building the road model solely from online sensor measurements. Based on Dempster-Shafer theory and a novel frame of discernment, sensor measurements, such as lane markings, semantic segmentation of drivable and non-drivable areas and the trajectories of other observed traffic participants are fused into semantic grids. Geometrical lane information is extracted from these grids via an iterative path-planning method. The proposed approach is evaluated on real measurement data from German highways and urban areas.

In the course of the development and integration of the autonomous driving the knowledge about the current environment and especially the road is one of the basic requirements to fulfill the automated driving task. This information is often extracted from a precise map provided in the vehicle. Therefore the road course and the individual lanes are known in advance by using the map and a suitable ego position estimation. However, in some situations such a map may be invalid and therefore unusable. This can be a driving area which was never mapped or regions where the map is outdated because the environment has changed. The following paper addresses the problem of building a road model without using a map by only fusing measurements from different sensors mounted on the ego vehicle. As sensor measurements various information like lane markings painted on the ground, the position of other cars or occupancy grids can be used. They are transformed into a grid-based model and a geometrical description is extracted out of this model by the use of a novel path-planning based method. The proposed approach was tested with a vehicle equipped with sensors and real measurement data from German highways.

The information about the road course and individual lanes is an important requirement in driver assistance systems and for automated driving applications. It is often stored in a highly accurate offline map so that the road and the lanes are known in advance. However, there exist situations where an offline map can become unusable or invalid. This paper presents a novel approach for a road model estimation solely based on online measurements from sensors mounted on the ego vehicle. It combines perception data like detected lane markings, the movement history of dynamic objects in the vehicle's environment and detected road boundaries into a grid-based road model. This approach allows for an estimation of the road model even when one source of information is not available and offers a redundant source of information about the road, which is necessary in critical applications such as automated driving. The presented approach was tested and evaluated with a prototype vehicle and real sensor data from German highway scenarios.

Mapping and tracking in dynamic environments for autonomously-moving robots is still challenging, despite being essential tasks. They are often done separately using occupancy grids and established object tracking algorithms. In this work, an approach is presented that estimates a uniform, low-level, grid-based world model including dynamic and static objects, their uncertainties, as well as their velocities. It does not require existing object tracks to filter out data points not used for creating and updating the map. Nor does it require that measurements can be classified into belonging to a static or to a moving object. Promising results from experiments with an autonomous vehicle equipped with a laser scanner demonstrate the usefulness of the approach.