Abstract

TIMON project (www.timon-project.eu) aims at providing Real-Time (RT) information and services through a cooperative open web-based platform and a mobile application to different transport users – drivers, Vulnerable Road Users (VRU), and businesses."br" Problems such as the traffic congestion and accidents cause cities to be the principal source of pollutant emissions. TIMON provides a solution by creating a cooperative ecosystem which connects people, vehicles, infrastructure and business, with the aim of improving safety, sustainability, flexibility and efficiency of the road transport."br" The system collects and stores RT data from infrastructure, cars, VRUs and open data; it processes the information using several innovative technologies, such as open data management, hybrid vehicle communications, artificial intelligence and cooperative positioning; and delivers RT services to drivers, VRUs and business particularly information on nearby accidents, traffic jams or air pollution, a multi-modal commuter and dynamic route planner.

Abstract

Traffic congestion and accidents cause cities to be the principal source of pollutant emissions. The TIMON project initiative aims at providing Real-Time (RT) information and cloud-based services through an open web-based platform and a mobile application to the main actors: drivers, vulnerable road users and businesses. TIMON establishes a cooperative ecosystem to connect people, vehicles, infrastructure and business and contributes to intelligent transport, IoT and Cloud computing. In the first part, this paper provides an overview of TIMON and how it contributes to increasing safety and reducing congestion and emissions. The TIMON ecosystem represents the perfect use case of distributed technologies, as it collects data from IoT sensors, open and closed data sources and user engagement data, processes it and provides useful information not only for road users, but also for scientists and technicians who need real systems to study the data, infrastructure and IT safety management. In the second part, the Cloud deployment of the TIMON system is described in detail and a new, more distributed design is proposed to exploit the potential of current emerging technologies of Fog and Edge computing. Document type: Conference object

Abstract

Modern automated and cooperative driver assistance systems (CoDAS) rely deeply on the position estimation. Regardless of absolute positioning accuracy, the relative position in regard to driving environment and other vehicles needs to be of high quality to enable sophisticated functions. Global Navigation Satellite Systems (GNSS) fulfill this demand only partially. In this paper we present an algorithm to accurately infer the driving lane by utilizing Dedicated Short Range Communication (DSRC) and map data alone. We evaluate our approach against simulated and real-life data from Europes largest cooperative vehicle Field Operational Test (FOT): simTD. This lane detection algorithm will be an enabler for CoDAS functions like collaborative driving and merging developed in the TEAM IP project.

Abstract

High-precision and lane selective position estimation is of fundamental importance for prospective advanced driver assistance systems (ADAS) and automated driving functions, as well as for traffic information and management processes in intelligent transportation systems (ITS). User and vehicle positioning is usually based on Global Navigation Satellite System (GNSS), which, as stand-alone positioning, does not meet the necessary requirements in terms of accuracy. Furthermore, the rise of connected driving offers various possibilities to enhance GNSS positioning by applying cooperative positioning (CP) methods. Utilizing only low-cost sensors, especially in urban environments, GNSS CP faces several demanding challenges. Therefore, this contribution presents an empirical study on how Vehicle-to-Everything (V2X) technologies can aid GNSS position estimation in urban environments, with the focus being solely on positioning performance instead of multi-sensor data fusion. The performance of CP utilizing common positioning approaches as well as CP integration in state-of-the-art Vehicular Ad-hoc Networks (VANET) is displayed and discussed. Additionally, a measurement campaign, providing a representational foundation for validating multiple CP methods using only consumer level and low-cost GNSS receivers, as well as commercially available IEEE 802.11p V2X communication modules in a typical urban environment is presented. Evaluating the algorithm&rsquo s performance, it is shown that CP approaches are less accurate compared to single positioning in the given environment. In order to investigate error influences, a skyview modelling seeking to identify non-line-of-sight (NLoS) effects using a 3D building model was performed. We found the position estimates to be less accurate in areas which are affected by NLoS effects such as multipath reception. Due to covariance propagation, the accuracy of CP approaches is decreased, calling for strategies for multipath detection and mitigation. In summary, this contribution will provide insights on integration, implementation strategies and accuracy performances, as well as drawbacks for local area, low-cost GNSS CP in urban environments. Document type: Article

Abstract

There is still limited understanding of people’s behaviours in transit situations, which results in a gap in smoothly"br" guiding users from a start to a destination in multi-modal transportation networks. Thereby an increasing number"br" of personal mobility scenarios require the localization of individuals or a group of end users within a group or"br" neighbourhood. Not rarely, this is the case in adverse operating conditions where only limited positioning"br" persons is not sufficient for real-time passenger guidance. A solution to this problem is referred to as Cooperative"br" Positioning (CP). In CP, end user localization is performed using its own measurement data plus any additional"br" information coming from neighbouring users in the form of inter-nodal ranges or other means. In this study,"br" absolute indoor localization of a user group is enabled with a novel differential Wi-Fi (Wireless Fidelity, a.k.a."br" WLAN) positioning approach. Similar to Differential GPS (DGPS), the received signal strength (RSS)"br" measurements of the Wi-Fi access points (APs) are corrected to reduce short- and long-time variations of the RSS"br" at the user side by the use of reference stations. In addition, continuous positioning is achieved by integration of"br" other smartphone sensors, such as accelerometer, magnetometer, gyroscope and barometric pressure sensor. From"br" the calculated positions, trajectories of the users are derived which are processed with machine learning methods"br" and analysed in view of the movement behaviour of the group. Using these trajectories guidance information is"br" derived with regard to a possible group subdivision and management on different routes, for instance, in the case"br" of large crowds during an emergency situation or service interruption. Thus, the traffic flow and security aspect is"br" fulfilled. A further main attention in this study is led on the support of people with limited mobility and visually"br" impaired. These challenging tasks can only be achieved if a positioning and tracking concept on the micro-level"br" in the stations is provided for all end-users. In the practical evaluation, static and kinematic tests using a number"br" of users carrying different smartphones were conducted in an indoor lab setting. Raspberry Pi’s served as reference"br" stations and APs together with inclusions of available and visible Wi-Fi APs. One user enabled the hotspot function"br" on his mobile device so that the other users can measure the RSS to derive inter-nodal ranges. It could be proven"br" that the new CP DWi-Fi approach outperforms conventional localization algorithms due to a significant"br" improvement of the indoor positioning and tracking accuracy.