Daniel Wilms

SPREAD GmbH

With almost every new vehicle being connected, the importance of vehicle data is growing rapidly. Many mobility applications rely on the fusion of data coming from heterogeneous data sources, like vehicle and ”smart-city” data or process data generated by systems out of their control. This external data determines much about the behaviour of the re...

Vehicle software architectures have been evolving over the last twenty years to support data-driven functionalities. Several enterprises from different domains are currently focusing on improving their data architectures by re-defining the underlying data models to enable core support for analytics and artificial intelligence. Moreover, a common de...

The variety of vehicle data has motivated contributors from the automotive industry to develop and maintain the so-called Vehicle Signal Specification. As the semantics of the specification are limited to a tree-like hierarchy and data types, it has been considered for the foundation of a more expressive model in an ontology form. Since the first d...

A vehicle produces a wide variety of data streams. To maximize their immediate use, we require to interpret their meaning and express it semantically. Putting data into a semantic representation is also known as semantization. On the one hand, existing approaches to analyze sensor data are often use-case specific and do not consider its streaming-n...

Software architectures in automotive have evolved over the last twenty years to support data-driven functionalities. Currently, several enterprises from different domains are focusing on improving their architectures by redefining the underlying data models to enable core support for analytics and artificial intelligence. Additionally, a common des...

Despite the high value of vehicle data, their "Big Data" characteristics make it necessary to apply an intelligent data management approach to fit the world of connected devices. Moreover, the existing variety of data formats implies the undesired repetition of data engineering pipelines. Although semantic technologies and graph data models have pr...

Modern vehicles produce big data with a wide variety of formats due to missing open standards. Thus, abstractions of such data in the form of descriptive labels are desired to facilitate the development of applications in the automotive domain. We propose an approach to reduce vehicle sensor data into semantic outcomes of dangerous driving events b...

The Web of Things offers a platform-independent solution for interacting with connected devices. An important vertical of the WoT is the transportation domain with, at its core, autonomous systems and among others, connected vehicles. They can be seen as complex artefacts , as they are composed of many sensors and actuators, legacy specifications a...

Modern vehicles are equipped with connectivity capabilities which will eventually open promising business opportunities. However , intelligent applications depend on high-quality and structured data which is difficult to obtain from the vehicle (i.e., several sensors, time-series data, different formats, etc.). Moreover, services and functions in t...

Application developers in the automotive domain have to deal with thousands of different signals, represented in highly heterogeneous formats, and coming from various car architectures. This situation prevents the development and connectivity of modern applications. We hypothesize that a formal model of car signals, in which the definition of signa...

We propose a car signal ontology named VSSo that provides a formal definition of the numerous sensors embedded in car regardless of the vehicle model and brand, re-using the work made by the GENIVI alliance with the Vehicle Signal Specification (VSS). We observe that recent progress in machine learning enables to predict a number of useful informat...

Car signal data is usually hard to access, understand and integrate for non automotive domain experts. In this paper, we use semantic technologies for enriching signal data in the automotive industry and access it through Web of Things interactions. This combination allows the access and integration of car data from the web. We built VSSo, a Vehicl...

In this paper, we use semantic technologies for enriching trajectory data in the automotive industry for offline analysis. We proposed to re-use a combination of existing ontologies and we designed a Vehicle Signal Specification ontology to provide an environment in which we developed an application that analyzes the variations of signal values and...

In this paper, we present the design and implementation of DrIveSCOVER, a recommender system for places and events in case of an in-car use, where the driving conditions such as weather and local traffic are taken into account. We integrate multiple data sources using semantic technologies and we devise recommending functions that are presented in...