WeD5 – Autonomuos Drivinggo to top
Wednesday, 19 June 2019, 16:00-17:30, Room 5
Session chair: Sandra Roger (Universitat de València, Spain)
Packet Inter-Reception Time Modeling for High-Density Platooning in Varying Surrounding Traffic Density
Guillaume Jornod (Volkswagen AG & TU Braunschweig, Germany); Ahmad El Assaad (Volkswagen, Germany); Andreas Kwoczek (Volkswagen AG, Germany); Thomas Kürner (Technische Universität Braunschweig, Germany)
A recent feature of communications systems is the agile quality of service adaptation, in which the application and the communications system exchange requirements and prediction of quality of service. The application first provides its quality of service requirement. The communications system tries to enforce it, and makes a prediction of the available quality of service. Finally, the application adapts its settings to the future quality of service and provides updated requirements. Though this concept is originally designed for cellular-based technologies, it is also applicable to ad-hoc communication systems. In this paper, we focus on the prediction of quality of service for ad-hoc communications in a high-density platooning system. The quality of service of interest is the packet inter-reception time in an IEEE 802.11p network. Our platooning system drives through different vehicular traffic conditions, in which we gather transmission and position data. We then analyze the distribution of the packet inter-reception time to select the model features and then fit multiple distribution models. This empirical prediction modeling will then be the baseline for future modeling.
Channel Models for the Simulation of Different RATs Applied to Platoon Emergency Braking
Tianxiang Nan (Technische Universität Braunschweig, Germany); Guillaume Jornod (Volkswagen AG & TU Braunschweig, Germany); Michael Schweins (Technische Universität Braunschweig & Institut für Nachrichtentechnik, Germany); Ahmad El Assaad (Volkswagen, Germany); Andreas Kwoczek (Volkswagen AG, Germany); Thomas Kürner (Technische Universität Braunschweig, Germany)
We analyze the performance of different channel models and Radio Access Technologies (RATs) for platoon emergency braking in a highway scenario. We present a ray tracing channel model and analyze its differences with the WINNER+ stochastic channel model in terms of the pathloss calculation. Thanks to the consideration of obstacles and their reflections, the ray tracing channel model as been shown to be more realistic in near Tx-Rx distance. This corroborates the results of our performance comparison which highlights larger differences in close Tx-Rx pairs. Considering the simulation time consumption and the more realistic ray tracing predictions, we propose a new models usage for our simulations: a combination of WINNER+ and ray tracing channel models. Moreover, we implement one new 5G numerology on the basis of Long Term Evolution-Vehicles (LTE-V) for Vehicle-to-everything (V2X) communications. We include this new feature in our benchmarking setup and provide performance analysis results. It provides a basis for our future research of further 5G components.
A Lane Merge Coordination Model for a V2X Scenario
Luis Sequeira, Adam Szefer, Jamie Slome and Toktam Mahmoodi (King’s College London, United Kingdom (Great Britain))
Cooperative driving using connectivity services has been a promising avenue for autonomous vehicles, with the low latency and further reliability support provided by 5th Generation Mobile Network (5G). In this paper, we present an application for lane merge coordination based on a centralised system, for connected cars. This application delivers trajectory recommendations to the connected vehicles on the road. The application comprises of a Traffic Orchestrator as the main component. We apply applies machine learning and data analysis to predict whether a connected vehicle can complete the cooperative manoeuvre of lane merge successfully. Furthermore, the acceleration and heading parameters that are necessary for completion of a safe merge, are elaborated. Results demonstrate performance of several existing algorithms and how their main parameters were selected to avoid overfitting.
Autonomous Driving Progressed by oneM2M – The Experience of the AUTOPILOT Project
Giovanna Larini and Giovanni Romano (Telecom Italia, Italy); Mariano Falcitelli (CNIT – Photonic Networks National Laboratory, Italy); Sandro Noto (CNIT, Italy); Paolo Pagano (Consorzio Nazionale Interuniversitario per le Telecomunicazioni, Italy); Miodrag Djurica (TNO, The Netherlands); Georgios Karagiannis (Huawei Technologies, Germany); Gürkan Solmaz (NEC Laboratories Europe, Germany)
The European Commission Horizon 2020 AUTOPILOT (AUTOmated driving Progressed by Internet Of Things) is aiming to exploit the IoT ecosystem to integrate connected cars and transform them in automated moving “objects”. One of the key challenges encountered in the project is to ensure interoperability of the different components and IoT platforms serving e.g. in-vehicle and road-side devices and sensors. The adopted solution is the use of Federated IoT platforms, with the oneM2M Interoperability Platform used to ensure that all components are able to communicate to each other. This paper provides a high-level description of the project and its goals and then concentrates on the importance to ensure interoperability support for IoT platforms using the standard IoT platform provided by oneM2M.
A Look into Communication and Necessary Ingredients for Improved Situational Awareness
Manzoor Ahmed Khan (TU Berlin, Germany); Martin Berger (Technische Universität Berlin, Germany); Xuan-Thuy Dang (Technische Universität Berlin & DAI Labor, Germany)
Automated driving is expected to enormously evolve the transportation industry and ecosystems. Advancement in communications and sensor technologies have further accelerated the realization process of the autonomous driving goals. There are a number of autonomous driving initiatives around the world with varying objectives and scope e.g., vehicle perception in a controlled environment or highway settings. Autonomous driving in urban environments with mixed traffic poses major challenges. The solutions for such environments is the focus of this paper. We start with a quick overview of current autonomous driving development activities worldwide. We then discuss the solution concept for autonomous driving in urban environments and its enabling components, e.g., road digitization and flexible communication infrastructure, to realize an urban autonomous driving testbed. We demonstrate an AI-based approach for the analysis of real traffic data measured on the testbed. The learned traffic demands provide valuable patterns for efficient, low delay management of various autonomous driving infrastructure operations, e.g., 5G mobile network, edge infrastructure, and vertical services.