Mobility 2.0: co-operative ITS systems for enhanced personal electromobility

Mobility2.0 is an ITS project aiming at developing and testing an in-vehicle commuting assistant for FEV mobility, resulting in more reliable and energy-efficient electro-mobility. In order to achieve a maximum impact, Mobility2.0 takes an integrated approach of addressing the main bottlenecks of urban FEV mobility: 'range anxiety' related to the limited FEV range, scarcity of parking spaces with public recharging spots and the congestion of urban roads. Our integrated approach means that the application developed by Mobility2.0 will utilize co-operative systems, through communication with transport/traffic city control systems and interaction with the charging infrastructure, to simultaneously consider these bottlenecks, so that such an optimization can be achieved which still guarantees reliable transportation for each FEV owner. The Mobility 2.0 envisioned application acts as a service platform that helps drivers to plan their trip and simultaneously manage the charge of their FEV through their personal nomadic device. This can be achieved by using real time information about the FEV parameters (e.g. vehicle dynamics and energy stored in the batteries), combined with external parameters (e.g. conditions of road traffic, public transport schedules, state of the grid) and learned driver profiles in order to both determine the range autonomy accurately but also provide a multi- modal commute trip recommendation to the FEV user. This paper provides an overview of the project's main objectives and the methodology to be used to achieve them

Next Generation Automated Emergency Calls

The Internet of Things (IoT) potentials to transform our modern society into smart environments that facilitate living and boost all types of transactions are becoming more and more evident as the number of interconnections between the physical and the virtual world keeps increasing. Cyber-physical systems, wide end-to end connectivity and handling of big data are some of the mainstream concepts brought forth to materialise the IoT umbrella. Yet, emergency services, a domain of paramount importance to society, reveal multiple challenges for the adoption of applications that capitalise on the capabilities of smart devices and the interoperability among heterogeneous platforms. In this paper, we present the continuing work [4] on next generation automated (non- human initiated) emergency calls by specifying the pathway to implementation of NG eCall and sensor-enabled emergency services

Driving style recognition for co-operative driving: a survey

Abstract—This paper serves as a critical survey for automatic driving style recognition approaches and presents “work in progress ” ideas that can be used for the development of intelligent context-adaptive driving assistance applications. Furthermore, a preliminary specification of a context-adaptive application that can be described by the following three steps is provided: at first, driving style is automatically classified into one out of a set of predefined classes that are learnt through historic driving and trip data; secondly, based on the driving style recognition a context-adaptive driving application is proposed; thirdly, eco-safe and co-operative driving behaviour can be rewarded by the system by introducing a serious game theoretic approach. While the focus of this paper lies on reviewing the state of the art for implementing the first step, providing the high-level specification of the two other steps offers valuable insight on the requirements of such collaborative driving application. Keywords- driving behaviour; vehicle dynamics; time-series analysis; supervised learning; classification; co-operative system. I

Mapping and evaluation for GPS restricted environments used for automated parking applications

In this paper, two state-of-the-art solutions to the simultaneous localization and mapping (SLAM) problem are implemented, depending on the environment type. A line feature-based solution using the extended Kalman filter is selected for structured environments, while for unstructured an incremental likelihood maximization algorithm using scan matching is adopted. This work proposes an evaluation method for the mapping accuracy assessment, able to handle results from different map representations. The resulting maps of both algorithms are compared to the digitalized areas blueprints after being converted to a common representation, which makes use of custom elements supported by the popular OpenStreetMap digital map format. Experiments were performed in two parking garages with different characteristics showing the applicability of the proposed evaluation method independent of the SLAM algorithm used

Co-operative commuting assistant for enhanced personal electromobility

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