Intelligent tires can be employed for a wide array of applications ranging from tire pressure monitoring to analyzing tire/road interactions, wheel loading as well as tread wear monitoring. In this paper we develop a measurement system for intelligent tires equipped with a 3-dimensional piezoresistive force sensor. The output of the sensor is segmented into tire revolution cycles, which are then represented by a transformation relying on adaptive Hermite functions. The underlying idea behind this step is to extract relevant features which capture tire dynamics. Then we evaluate the proposed measurement system in a potential vehicle application, that is, abnormal road surface detection. We deal with the corresponding binary classification problem by developing both low-complexity analytical and data-driven machine learning algorithms, which are tested on real-world measurement data. Our experiments showed that the proposed methods are able to detect abnormalities on the road surface with a mean accuracy of over 97%.

Adaptive suspension control considering passenger comfort and stability of the vehicle has been researched intensively, thus several automotive companies already apply these technologies in their high-end models. Most of these systems react to the instantaneous effects of road irregularities, however, some expensive camera-based systems adapting the suspension in coherence with upcoming road conditions have already been introduced. Thereby, using oncoming road information the performance of adaptive suspension systems can be enhanced significantly. The emerging technology of cloud computing enables several promising features for road vehicles, one of which may be the implementation of an adaptive semi-active suspension system using historic road information gathered in the cloud database. The main novelty of the paper is the developed semi-active suspension control method in which Vehicle-to-Cloud-to-Vehicle technology serves as the basis for the road adaptation capabilities of the suspension system. The semi-active suspension control is founded on the Linear Parameter-Varying framework. The operation of the presented system is validated by a real data simulation in TruckSim simulation environment.

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Abstract-The race among manufacturers to build convenient, safe, and autonomous Connected Cars by applying the latest digital technologies, and ultimately a completely self-driving vehicle, is already underway. One of the cornerstones of such vehicles is the continuous ingestion of massive amount of data from wide variety of hardware components, including sensors, onboard cameras, and further external sources. Cloud computing and big data processing are ideal candidates and already proven technologies in order to store and process the heterogeneous, rapidly growing, and large-scale data sets. The cloud may act as a kind of central hub or as an Internet of Things (IoT) back-end where the sensor and the other available data can be gathered while also offering an elastic platform where the vast amount of data can be processed, analyzed and distributed real-time. In our paper we detail the evolution of a cloud-based, scalable IoT back-end framework and services built on top for handling and processing vehicular data in various use case scenarios: CAN data collection, remote device flashing, Eco-driving, weather report and forecast. The first version is an Infrastructure-as-a-Service (IaaS) solution with a reference implementation deployed on an OpenNebula based cloud. The second iteration runs on a private Platform-as-a-Service (PaaS) cloud built on the Cloud Foundry platform within the premises of an automotive supplier company. Both variants have been successfully evaluated and validated with benchmarks.