Extending the vehicular network simulator Artery in order to generate synthetic data for collective perception

Abstract

Abstract. A fundamental for an automated driving car is the awareness of all its surrounding road participants. Current approach to gather this awareness is to sense the environment by on-board sensors. In the future, Vehicle-to-X (V2X) might be able to improve the awareness due to V2X's communication range superiority compared to the on-board sensors' range. Due to a limited amount of communication partners sharing their own ego states, current research focuses particularly on cooperative perception. This means sharing objects perceived by local on-board sensors of different partners via V2X. Data collections using vehicles, driving on real roads, is challenging, since there is no market introduction of cooperative perception yet. Using test cars, equipped with the required sensors are rather expensive and do not necessarily provide results representing the true potential of cooperative perception. Particularly, its potential is highly dependent on the market penetration rate and the amount of vehicles within certain vicinity. Therefore, we consider to create synthetic data for cooperative perception by a simulation tool. After reviewing suitable simulation tools, we present an extension of Artery and its counterpart SUMO by modelling realistic vehicle dynamics and probabilistic sensor models. The generated data can be used as input for cooperative perception.